Vol. 22 No. 3 (2025)

Aiming at the problems existing in the water supply and drainage design of high-rise buildings, this paper starts with the application advantages and design process of BIM technology in the water supply and drainage design of high-rise buildings. Suggestions for promoting the sustainable development of science and technology are put forward for the reference of relevant persons in charge. In the architectural design industry, architectural design is not only a literary and artistic creation, but also a comprehensive engineering project involving multiple industries. It mainly contains a large amount of information, which must be collected, classified, analyzed, searched and transmitted by powerful technical means. Building information entity model BIM technology is a new concept, new concept and new technology existing in data building technology, which brings a strong technical support point for the development trend of building customization. This paper discusses the energy -saving design application of this small high-rise residence based on BIM technology. This paper also integrates the principles of biomechanics and biomimicry to further enhance the application of BIM technology in green energy-saving design. By simulating biological structures and ecosystems in nature, it optimizes the building’s energy management and structural performance, thereby designing more efficient and sustainable architectural solutions.

Integrating artificial intelligence and advanced biosensor technologies represents a transformative paradigm in athletic performance optimization. This research explores the revolutionary potential of AI-driven fitness solutions to redesign training methodologies across professional and amateur sports disciplines fundamentally. These technologies offer unprecedented capabilities for personalized, data-driven athletic development by addressing critical limitations in traditional performance tracking. The study examines comprehensive approaches to physiological monitoring, performance prediction, and individualized training interventions enabled by advanced machine learning algorithms and sophisticated biosensor technologies. Key innovations include real-time physiological data collection, predictive performance analytics, and adaptive training strategies that maximize individual athletic potential while minimizing injury risks.

The rapid development of modern society and heightened competitiveness have led to increased expectations from parents, educators, and society regarding college students. This environment, coupled with academic pressures and employment challenges, significantly affects students’ physical and mental health. This paper employs big data analysis to explore the intrinsic connections between physical activity and mental health, incorporating biomechanical insights into the discussion. Biomechanics examines the mechanical aspects of human movement, providing a deeper understanding of how physical activity influences mental well-being. Engaging in regular physical activity enhances physiological responses, such as improved circulation, increased endorphin release, and reduced stress hormones, all of which contribute to better mental health outcomes. In our study, we utilized the SCL-90 for mental health assessment and conducted a survey on cognitive characteristics related to sports participation. A sample of 500 university students was analyzed to establish a behavioral cognitive model of sports activity participation. Correlation coefficients revealed that the intensity (0.1354) and duration (0.2455) of physical activity correlate positively with mental health scores. Furthermore, factors such as frequency and total volume of physical activity demonstrated varying degrees of correlation across five mental health dimensions. Regression analysis yielded a standardized coefficient of 0.6154, indicating that physical activity participation significantly positively influences mental health scores. By integrating biomechanical principles, this research highlights the importance of movement efficiency and physical engagement in promoting mental health, suggesting that enhancing physical activity can serve as a vital strategy for improving overall well-being among college students.

Such a study in the biomechanical characterization of the movements involved in directional dribbling can be undertaken with adolescent basketball players to provide evidence in training recommendations. It demonstrates complex interactions between different biomechanical components during directional dribbling by using large-scale analysis of joint kinematics, dynamic parameters, and movement control strategies. The movement execution and performance outcome are mostly influenced by individual, technical, and environmental factors. A structured training program combining physical conditioning, technical skill development, and injury prevention strategies was implemented and evaluated over a 12-week period. The outcomes of this program demonstrated significant improvements in performance factors, including movement efficiency, accuracy, and decision-making. These findings contribute to the theoretical understanding and applied implications in basketball training and offer key information for coaches and practitioners working with adolescent athletes.

Purpose: This study aimed to scientifically develop the Four-Dimensional Job Performance Evaluation Questionnaire for Clinical Nurses from a biological aspect and to assess its reliability and validity. Methods: Guided by Koopmans’ four-dimensional work performance theory, the questionnaire was structured around task performance, relational performance, adaptive performance, and counterproductive performance. The initial version was developed through a comprehensive literature review, analysis of hospital performance evaluation indices, and Delphi method consultations with experts. A survey was conducted among 549 clinical nurses in a tertiary hospital in Xinjiang Province, and the questionnaire’s reliability and validity were evaluated using the critical ratio method, factor analysis, and reliability and validity tests. Results: The content validity index (I-CVI) for each item ranged from 0.820 to 1.000, with an average level content validity index (S-CVI/Ave) of 0.970. The overall Cronbach’s alpha coefficient for the questionnaire was 0.962, with dimension-specific coefficients of 0.967, 0.901, 0.953, and 0.909. Exploratory factor analysis indicated a cumulative variance contribution rate of 63.235% for the four principal factors, and confirmatory factor analysis confirmed a good model fit, leading to the finalization of a 45-item questionnaire covering four dimensions of clinical nurses’ job performance. Conclusion: The Four-Dimensional Job Performance Evaluation Questionnaire for Clinical Nurses developed in this study demonstrates good reliability and validity, offering a comprehensive measurement tool for assessing nurses’ job performance levels in the biological context.

This study focuses on leveraging transfer learning technology to revolutionize fitness training from a cell and molecular biomechanics perspective. In the era of advanced biotechnology, understanding the minute biomechanical events within cells during exercise is crucial. We aim to apply computer-intelligent control concepts to fitness training, especially aerobics, by delving into the cell and molecular biomechanics. Via in-depth analysis of aerobics training's impact on cells and molecules and smart use of computer tech, a B/S mode simulation model integrating NET and SQL Server is crafted. This model offers a scientific framework for fitness training centered around cell and molecular biomechanics. The ID3 algorithm is then employed to dissect student sports test data related to cell and molecular changes, enabling personalized training plans based on individual cell and molecular traits. To enhance the model, the association rule algorithm is introduced. By scrutinizing extensive cell and molecular biomechanics training data, such as how mechanical forces influence gene expression and protein interactions, hidden patterns and correlative factors are unearthed. This refines the model's accuracy and practicality. During experimentation, comprehensive testing of the association rule algorithm in the context of cell and molecular biomechanics is carried out. Results confirm the viability of the computer-intelligent control-based aerobics training strategy, which effectively boosts fitness training effectiveness at the cell and molecular level. This research pioneers novel approaches for aerobics and other sports, providing valuable insights for optimizing training with respect to cell and molecular biomechanics.

This study investigates the dynamic mechanisms underlying creativity development through martial arts movement drawing training and combined with relevant knowledge of biomechanics, emphasizing cognitive adaptability and neurophysiological engagement. Using experimental methods and independent sample t-tests, we assessed differences across five creativity sub-dimensions—fluency, originality, flexibility, sensitivity, and insight—between an experimental group and a control group. The results showed significant improvements in the experimental group following the training, whereas no substantial changes were observed in the control group. These findings indicate that martial arts movement drawing training not only involves observing, analyzing, and expressing dynamic actions, but also requires participants to coordinate various parts of the body during movement. This biomechanical interaction promotes the body’s movement efficiency and coordination, promotes perception, imagination, and cognitive flexibility, thereby enhancing creativity. By integrating creative expression with motor and sensory coordination, this study underscores the potential of dynamic, art-based interventions to enhance cognitive adaptability and functional plasticity. Principles of biomechanics, such as kinematics and kinetics, can further explain how participants’ training can promote the development of brain function by optimizing movement patterns and improving motor control. This research offers an innovative approach to creativity development, with implications for interdisciplinary studies in cognition, biomechanics, and artistic training.

The integration of bioinformatics, biological adaptations and biomechanics into public physical education offers a promising direction for enhancing sport science education. This article comprehensively explores the evolution of historical research in sport, not only through the lens of biological concepts but also incorporating biomechanical principles. It focuses on how adaptations, bioinformatics, and biomechanical analysis work in tandem to deepen our understanding of sport. By analyzing 514 articles published between 2000 and 2019, the study identifies trends in the study of sport history, particularly in the context of athletes’ biological adaptations and their application in monitoring and enhancing physical performance techniques. Using CiteSpace visualization software, this paper constructs a knowledge map of key research themes and influential journals, demonstrating the dynamic development of the field. The research highlights how the biomechanical principles of adaptability, biological resilience and performance optimization are increasingly integrated into the study of sport history and physical education. Key findings include a staged trend in sport history research. The significant impact of biological adaptations, informed by biomechanical analysis, on training methods is evident. Bioinformatics further aids in processing and making sense of the vast amounts of biomechanical data collected. These insights suggest that integrating biological approaches into physical education teaching can significantly improve the theoretical and practical outcomes of physical education teaching in higher education. In addition, in an international comparative perspective, in the United States and Germany, college physical education teaching focuses more on optimizing athletes’ performance through wearable technology and biomechanical modeling, whereas Japan and China pay more attention to the integration of traditional training methods with bioinformatics and biomechanical technology. This cross-national comparison not only reveals the characteristics of different countries in sport science education, but also provides a more comprehensive global perspective for future research.

With the rapid evolution of educational technology, teaching methods have transcended traditional face-to-face instruction. Beyond physical classrooms, learners now benefit from the flexibility and accessibility of online learning environments. This research examines the innovative dynamics within online collaborative learning classrooms for football training courses, analyzed through a biomechanical lens. Utilizing an action research methodology, the study investigates strategies to cultivate creativity and innovation in virtual football training courses, with a focus on biomechanical principles such as motion analysis, kinetic chain efficiency, and neuromuscular coordination. Key approaches are highlighted: fostering an engaging and innovative classroom atmosphere as a cornerstone for enhancing students’ creativity and biomechanical understanding; ensuring learners have access to appropriate personal equipment, such as motion capture devices or wearable sensors, to facilitate accurate biomechanical data collection and analysis during online participation; leveraging course platforms to document and facilitate interactions between learners and instructors, particularly in the context of movement optimization and injury prevention; and addressing the specific requirements of biomechanics-oriented online teaching. Furthermore, the integration of “collaborative learning” and “problem-oriented learning” emerges as the most impactful approach to nurturing creativity and biomechanical proficiency among learners in this context. This study highlights the potential of combining biomechanical principles with online collaborative learning to enhance the quality and innovation of football training education.

In the realm of physical education in higher education institutions, dance courses have emerged as a vital component due to their holistic nature and technical demands. However, traditional teaching methods often face challenges, including limitations in teaching resources, learning interaction, and motion correction. To address these shortcomings and enhance teaching effectiveness, this study introduces a biometric and motion analysis system tailored for sports dance instruction. Grounded in biomechanical principles and leveraging wearable devices alongside intelligent mobile terminal technology, the system collects kinematic and dynamic data from students’ dance movements. By employing biomechanical models, it quantitatively evaluates movement standardization and provides real-time feedback to students. The research findings demonstrate that this innovative system significantly improves teaching interactivity and student movement accuracy, achieving a 14% increase in teaching efficiency. Furthermore, 93% of students expressed high satisfaction with the system. This study advocates for the integration of mobile intelligent terminals and biometric technology, the optimization of course design, the development of teaching resources guided by biomechanics, and the strengthening of the synergy between practice and theory. By doing so, it aims to establish a more scientific and effective sports dance teaching model.

With the continuous advancement of technology, intelligent sports technology has gradually become an important tool in sports training. This study aims to explore the application of intelligent sports technology in optimizing the movement training of kayaking athletes. By introducing advanced technologies such as motion capture, data analysis, and virtual reality, the research aims to improve athletes’ coordination and stability in their movements. Using kayaking athletes as research subjects, this study provides a detailed description of the application methods and experimental design of intelligent sports technology and systematically analyzes the collected data. The research results show that intelligent sports technology has a significant effect on improving the precision and efficiency of athletes’ movements. Specifically, through real-time feedback and data accumulation, coaches and athletes can develop more scientific and reasonable training plans, thereby significantly enhancing training effectiveness. However, the study also points out the shortcomings of intelligent sports technology in terms of portability and real-time data processing, which need further improvement and optimization in future research. Overall, this study provides evidence for the application of intelligent sports technology in kayaking training, having important practical significance and application value. It offers valuable references for the future development of sports training and intelligent technology.

Art has been a medium of self-expression, evolving with technological advancements. Using physiological signals, biometric painting directly affects the artistic process. By bridging the gap between the artist’s internal emotional state and the visual depiction of the painting, this fusion provides an innovative approach to examining and expressing human emotions. The objective is to investigate biometric painting, integrating biosensor data into the creative process. To expand the creative process of biometric painting by utilizing biosensor data to establish emotion recognition in biometric painting. A biometric painting system was created that used users’ real-time biosensor data to gather visual components that represented their emotional and physical states. The data is preprocessed using a median filter to remove noise from the sensor data. Then, the features are extracted using wavelet transform (WT). The research introduces an Intelligent Remora Optimized Flexible Deep Belief Network (IRO-FDBN) to recognize emotion in biometric painting using biosensor data. The results indicate that the established model outperforms an emotion recognition model. The approach emphasizes the smooth combination of visual and affective feedback, allowing audiences to engage with the artwork on an advanced level. This provides a foundation for incorporating biosensor data into the creative process, advancing artistic exploration and effective content development.

Background: The transformation of physical education in Chinese higher education institutions necessitates empirical evaluation of reformed teaching models’ effectiveness in promoting student health outcomes. This study investigates the longitudinal impact of innovative physical education reforms on students’ physical fitness parameters, emphasizing biomechanical adaptations alongside physiological changes. Methods: A controlled longitudinal study was conducted across three universities in Eastern China, involving 426 undergraduate students (213 intervention, 213 control) over 18 months. Comprehensive physical fitness assessments were performed using standardized protocols, measuring cardiovascular endurance, muscular strength, and body composition. Statistical analyses included repeated measures ANOVA, multivariate regression, and time series analysis. Results: The intervention group demonstrated significantly superior improvements in cardiovascular fitness (ΔVO₂max: +4.8 ± 1.2 vs. +2.1 ± 1.1 mL/kg/min, p < 0.001) and muscular strength parameters. Strong correlations between program participation and fitness outcomes (r = 0.68, p < 0.001) were observed. Longitudinal analysis revealed three distinct adaptation phases: initial rapid improvement, plateau phase, and sustained enhancement. Conclusions: The reformed physical education model effectively enhanced student physical fitness across multiple parameters, with sustained improvements throughout the intervention period. These findings provide empirical support for the implementation of innovative teaching methodologies in higher education physical education programs and highlight the critical role of biomechanical adaptations in understanding the effectiveness of these reforms.

Aerobic exercise is recognized for its multiple health advantages, which include increased cardiovascular endurance, metabolic efficiency, and mental well-being. Aerobic exercise is important for college students because it promotes general physical health and stress management during a pivotal period in their lives. The objective of this research is to analyze and compare the impact of different types of aerobics training on sports injury risk among college students. A total of 235 college students participated in this analysis; they were randomized and separated into four distinct groups: three experimental groups (EG), such as traditional aerobics training, high-intensity interval (HII) aerobics training, and dance-based aerobics training, and a control group (CG) received no aerobics training. The research consumed 8 weeks, with each group completing their allocated training mode two times per week. Self-reports, physical examinations (muscle tiredness, joint strain), and fitness tests were utilized to evaluate the risk of injuries. The data was analyzed using statistical methods and SPPS software. The findings suggest that high-intensity interval aerobics significantly increased fitness; they also increased the risk of injury, especially to the lower limbs. While traditional aerobics training showed modest improvements and a decreased injury rate, dance-based aerobics offered balanced fitness and injury prevention advantages, as well as increased joint mobility and flexibility. The CG demonstrated no significant changes in injury rates or fitness improvements. This analysis emphasizes how crucial it is to customize aerobics programs to each participant’s level of fitness reduce the risk of injury and maximize health benefits.

This research delves into the intricate relationship between cellular and molecular adaptations and their implications in educational advancements among adolescents engaged in sports dance routines within the framework of physical education settings. Utilizing a randomized controlled trial involving 120 participants (with 60 in the experimental group and 60 in the control group), the study spanned a 16-week intervention period. The assessment protocols were comprehensive, covering cellular and molecular indicators (biomechanical properties of muscle fibers, cardiac health markers), as well as educational outcomes (deliberate skill acquisition, academic engagement, and innovation in movement expression). Notably, the experimental group showed marked advancements across various parameters: an increase in cardiac health indicators (peak oxygen consumption, VO2max improvement: 21.3%, p < 0.001), enhanced biomechanical properties (muscle elasticity improved by 50%, muscle strength increased by 45%, p < 0.001), and an improvement in motor skills (technical proficiency rose by 41.5%, coordination by 48.3%, p < 0.001). There was a striking link between these cellular and molecular adaptations and the educational outcomes (r = 0.721–0.845, p < 0.01), which was further confirmed by regression analysis indicating cardiac metabolic fitness as a pivotal predictor of technical dexterity (β = 0.384, p < 0.001). The adolescents in the experimental group also exhibited considerable gains in mastering complex movement sequences (56.7% improvement, integration of performance: 55.3%, p < 0.001) and in academic engagement (motivation increased by 46.2%, collaborative interaction: 47.7%, p < 0.001). A pivotal “adaptive window” identified between the 8th and 12th week of training, suggested the most fruitful times for intervention to yield optimal outcomes. These results provide solid evidence that structured sports dance training is beneficial for both biomechanical development and educational success in adolescents, providing valuable guidance for physical education curriculum design and implementation.

This study aims to explore the application of sports sensors in motion correction within competitive sports, providing both theoretical and practical guidance. With the advancement of technology, sensor technology is increasingly being applied in sports training and competition, aiding athletes and coaches in more accurately analyzing movements, enhancing performance, and reducing injury risks. The research employs both qualitative and quantitative methods to collect and analyze data from athletes during training and competition, examining the roles of sensors in stride length, stride frequency, posture analysis, and real-time feedback. Additionally, through the formulation of personalized training plans and injury prevention strategies, the study showcases the practical effects of sensors in various sports disciplines. The findings indicate that sensor technology significantly improves movement optimization, injury prevention, and performance enhancement. However, the study also highlights challenges in data collection and analysis, suggesting future research to expand data samples and improve analysis models. This research not only offers new perspectives for theoretical exploration but also provides concrete guidance for practical applications, thus holding substantial practical significance.

This study aims to explore the development of interactive motion graphics themed around the “24 solar terms” from a biomechanical perspective, integrating user experience design with biorhythm concepts. Biorhythms refer to the biomechanical changes in humans, animals, and plants in response to different solar terms. The 24 solar terms, a significant part of traditional Chinese culture, not only reflect the ecological behaviors of animals and the biomechanical adaptations of plants to environmental changes but are also closely linked to the biomechanical variations in human physiological states. By incorporating biomechanical principles and bionic technology into motion graphic design, the study seeks to create interactive experiences that are more aligned with biological adaptability, catering to users’ physiological needs and cultural identity. The study begins by analyzing the cultural significance of the 24 solar terms and their impact on human life, revealing their critical connection to the biomechanics of human physiological states, such as dietary habits, sleep patterns, and emotional conditions. Based on these findings, a novel motion graphic design method is proposed, leveraging deep learning techniques grounded in bionic principles (e.g., the VGG-19 model) to monitor and analyze users’ biorhythms in real-time. This enables dynamic adjustments to graphic content and interaction modes, enhancing user immersion and engagement. Furthermore, the study explores how information modeling and reverse engineering techniques can digitize traditional cultural elements and biomechanical characteristics associated with different solar terms, creating interactive graphics that combine cultural depth with biological adaptability. This design not only improves user experience but also provides new perspectives and methods for the preservation of cultural heritage. The effectiveness of the proposed method is validated through analysis in terms of user satisfaction, interactivity, and information transmission efficiency.

With the development of economy, the public’s demand for cultural life is increasing. Sculpture brings a dynamic experience to the urban landscape, enabling people to actively participate and integrate into the landscape space, thus adding to the urban cultural construction and enhancing the variability and depth of space. Based on the principle of bionic design, this paper compares the degree of similarity of the original organisms, and divides the morphology bionic into figurative bionic, abstract bionic, and metaphorical bionic. Combined with the structural stress characteristics, the spatial finite element model is established, and according to D’Alembert’s principle, the vibration equilibrium equation is associated, and the vibration mode superposition method power analysis method is applied to analyze the modal state of a sculpture. The results show that: The horizontal direction of the vibration mode coefficient has reached more than 0.9, the period and frequency of the sixth and seventh order vibration modes are the same, both are 0.3948 s and 2.5699 Hz. Calculating the ultimate bearing capacity of the structure under all the conditions, the maximum stress ratio of the strength of each member of the sculpture is 0.96987, which indicates that the strength of all the structural members meets the requirements of the design of the sculpture. The overall stability of the sculpture is analyzed, when the order is 5, the buckling factor under three kinds of loading conditions are 208.5974, 114.1648, 124.9748, which indicates that the sculpture structure designed according to bionics has good overall stability.

In 2019, a sudden outbreak of novel coronavirus disease swept the world, seriously affecting education and teaching. Most universities around the world have adopted a “learning without classes” model, which is dominated by online teaching. Up to now, this teaching model under the normalization of the epidemic has been popularized all over the world. However, the evaluation of online teaching quality and the weight of influencing factors have become difficult points in measuring teaching quality, that is, the factors and weights affecting online teaching are problems that need to be studied and solved urgently. This study takes online physical education (PE) teaching in Chinese universities as the research object. While traditional research often centers on teaching evaluation methods, this research innovatively integrates biomechanics into the study. By analyzing the relevant research literature, it proposes a weight evaluation method for online PE teaching index in universities based on analytic hierarchy process (AHP)-entropy method-fuzzy comprehensive evaluation method. This method is actually a new teaching evaluation model that combines the subjective and objective weighting method with the fuzzy evaluation method. Integrating biomechanics offers a novel perspective on online PE. It aids in assessing students' exercise effectiveness and optimizing online educational resources. Advanced video analysis and motion capture can precisely measure students' joint angles, limb movement trajectories, and muscle activation patterns during exercises. This enables accurate evaluation of movement standardization. By comparing with optimal biomechanical models, teachers can provide targeted guidance, enhancing exercise effectiveness and reducing injury risks. In teaching video creation, incorporating biomechanical principles helps students understand the scientific basis of movements. For instance, in a basketball shooting tutorial, explaining the arm, wrist, and finger biomechanics can improve students' understanding and performance. Moreover, biomechanical simulation technology can create virtual sports scenes, enriching the online learning experience by allowing students to explore environmental impacts on body mechanics. We applied the method to the weight analysis of online PE teaching index in Chinese universities, and demonstrated that the method has good applicability. More importantly, we have condensed the conclusions of this research into practical countermeasures, and put forward strategies to improve the quality of online PE teaching from the macro level and the subjective research level. This research achieves innovation at the application level, improvement at the theoretical level and focus at the practical level. The research results can help Chinese universities to improve the quality of current online PE teaching, and even provide experience and reference for formulating relevant measures for international online teaching.

Martial arts include various fighting techniques, ideologies, and training programs from different civilizations worldwide. Combining mental and physical training, martial arts can be utilized for sport or self-defense. The research aims to investigate how martial arts training affects joint stability and muscular strength. A total of 146 respondents participated in this study. They are arbitrarily separated into two groups. Group A (n = 76) received martial arts (karate) training, and Group B (n = 70) received standard sports training. All participants underwent pretesting and post-testing focused on physical attributes, upper extremity flexibility, muscle strength, motivational level, balance, and joint stability. The data is analyzed using statistical methods such as correlation analysis, t-tests, and one-way ANOVA to compare pre-and post-training results. The post-training evaluations revealed that the karate group demonstrated significant improvements in joint flexibility and balance. These enhancements in flexibility, motivational level, balance, and strength are critical as they contribute to muscle strength and joint stability, essential for preventing sports-related injuries during growth. This study underscores the importance of martial arts training in developing physical fitness attributes that promote overall musculoskeletal health in children.

In recent years, the digitalization of ancient books has revitalized traditional works and highlighted the “humanistic” dimension of digitalization. This article employs the SikuBERT pre-training model, which is tailored for natural language processing in classical Chinese texts, and forges an innovative connection with the realm of biomechanics, to conduct a comprehensive analysis of spatial verbs in the pre-Qin dynasty text Zuo Zhuan. The analysis encompasses detailed annotation, quantitative analysis, automatic recognition, and evaluation of spatial verbs, culminating in the creation of a digital knowledge base for spatial verbs in Zuo Zhuan. The study identifies four main types of high-frequency spatial verbs in Zuo Zhuan: Motion, state, existence, and direction. To enhance theoretical depth, this classification is grounded in cognitive linguistic theory, which explains the semantic connotations and cognitive basis of each verb type. Motion verbs, are closely linked to the representation of dynamic spatiotemporal contexts, particularly in describing human movement patterns and behaviors, invoking principles from biomechanics such as kinematics and dynamics. These verbs can be integrated with biomechanical concepts like trajectory analysis and mechanical models to understand how ancient humans engaged in dynamic activities within specific spaces. State and existence verbs emphasize static relationships, while directional verbs highlight the guiding nature of movement trajectories, which can be further explored through concepts of displacement and velocity in biomechanics. These verb types interact to construct the spatiotemporal framework of the text, demonstrating how language encodes complex spatial and temporal relationships. Moreover, the study investigates the interactive mechanisms of the four verb types in language use, analyzing how they collectively construct the spatiotemporal context of ancient texts, thereby enhancing our understanding of narrative techniques in pre-Qin literature. For instance, motion verbs often act as the primary drivers of narrative progression, while state verbs provide contextual stability, existence verbs denote presence, and directional verbs guide interpretative focus. Quantitatively, the study examines the characteristics of these verbs from three dimensions: temporal-quantitative relations, behavioral-quantitative relations, and scene-component relations. The findings reveal distinct quantitative features among the four types, with motion verbs exhibiting the highest diversity and quantity. This nuanced exploration not only contributes to the re-interpretation of pre-Qin dynasty texts but also strengthens the ability to deconstruct and analyze digitalized texts from this period, thereby advancing the field of classical Chinese digital humanities. By integrating a biomechanical perspective, this study further explores the application of spatial verbs in describing human movement behaviors, utilizing biomechanical models to analyze the efficiency and postural changes of ancient humans in specific environments, revealing the deep connections between language expression and human activity. This interdisciplinary perspective enriches our understanding of ancient culture and provides a new methodological framework for research in modern humanities.

The goal of this study is to explore the biomechanical mechanism of digital music teaching resources in improving students’ musical expression, and to study how to optimize music teaching effect by means of technical means. By introducing collaborative filtering (CF) algorithm into the field of music education, a individualized teaching resource recommendation system is constructed. The system deeply analyzes students’ learning behavior, interest preference and learning effect, so as to achieve accurate matching of resources. In order to verify the effectiveness of digital teaching resources and recommendation system, a semester-long empirical study was designed and implemented. Select 100 music majors and divide them into traditional teaching resources group and digital teaching resources group. The study focuses on the differences between the two groups in mastering music theory, improving practical skills (especially musical expression in biomechanics) and stimulating learning interest. The results show that the students’ musical expressive power (especially the skills related to biomechanical mechanism) and learning interest in the digital teaching resource group are significantly improved, and the effect is far better than that in the traditional teaching resource group, which proves the great potential of digital teaching resources and individualized recommendation system in music education.

This paper discusses the impact of enterprise digital transformation on employee health management from the perspective of biomechanics, especially the change law of employee physiological response and the underlying mechanism under the new work mode and technology application. By introducing a theoretical framework of biomechanics, this study evaluates the specific impact of changes such as office automation, remote work and the use of smart devices on the physical load of employees, and uses computer modeling technology to predict the potential health risks under different working conditions. In this study, a hybrid model combining Probabilistic neural Network (PNN) and Gated Recurrent Unit (GRU) was used to deal with complex time-series data analysis tasks to improve the prediction accuracy of employee health status. Experimental results show that the proposed PNN-GRU model performs well in the task of health state recognition, especially in fatigue and pain detection, with the accuracy of 94.7% and 97.1% respectively, which is significantly better than other algorithms.

This study explores how the development of digital finance indirectly promotes the biomechanical adaptability and innovation ability of residents’ behavior by influencing consumption structure and payment environment. Data analysis shows that digital finance not only improves the utilization rate of personal financial services, but also generates significant heterogeneity effects among different regions and income groups. These effects can be understood from the perspective of biomechanics, which is the process by which individuals adapt to a rapidly changing technological environment. In biomechanical principles, adaptability is the key to survival. Digital finance encourages residents to optimize resource allocation, enhance consumer flexibility, and improve decision-making efficiency. This adaptation process is similar to the process by which organisms adjust their mechanisms to maintain survival and development in the face of changes in the external environment. The popularity of digital finance enables individuals to respond to market changes more quickly, thereby improving their adaptability in consumption. In addition, entrepreneurial activities and innovation levels serve as mediating variables, further stimulating residents’ adaptability in the digital ecosystem, similar to the mechanism in biomechanical systems that improves survival rates through diversity and innovation. This study reveals the important role that digital finance plays in modern consumer behavior and proposes that it may become a key driving force for the evolution of socio-economic systems and human behavioral biology. By introducing the principles of biomechanics, we can have a deeper understanding of how digital finance shapes changes in individual behavior and social structure, and thereby provide theoretical support for policy making and practice.

The new quality productive forces provide scientific guidance for countries to promote high-quality development and represent advanced productive forces in line with the new development concept. It is an inevitable requirement for adapting to the transformation of China’s economic development stage and a strategic measure to cope with the increasingly fierce international competition. Existing research primarily focuses on the theoretical connotations, characteristics, formation logic, and industrial practices of new-quality productive forces, but lacks comparative analysis and literature reviews of domestic and international studies. Drawing inspiration from the principles of biomechanics, this study delves into the intricate mechanisms underlying the rise of these advanced productive forces, aiming to unravel their potential to empower sustainable economic development. Akin to the dynamic interplay of form, function, and adaptation observed in biological systems, the new-quality productive forces embody the harmonious integration of scientific guidance, technological innovation, and market-driven optimization. Just as the human body's musculoskeletal system leverages the principles of force transmission and load distribution to achieve efficient movement, these productive forces harness the synergistic power of knowledge, technology, and market forces to drive economic progress. This paper employs the CiteSpace knowledge map tool to analyze the publication volume, author collaboration networks, keyword clustering, timelines, and emergent words from relevant literature in the core databases of CNKI and Web of Science. Drawing inspiration from biomechanics, the study highlights the importance of balancing the top-down and bottom-up forces that govern the formation and transformation of new-quality productive forces. Much like the human body's ability to adapt to changing environmental conditions, the successful integration of these productive forces into the economic landscape requires a delicate interplay of strategic planning, technological innovation, and market-driven optimization. By aligning the insights from this research with the principles of biomechanics, the study offers a unique perspective on the sustainable development of the economy. Just as biological systems exhibit elegant and efficient mechanisms to harness energy and resources, the new-quality productive forces hold the potential to empower countries to navigate the increasingly fierce international competition and achieve long-term, high-quality economic growth.

The computational complexity of graphic processing algorithms is increasing under the continuous development of information technology. At the same time, the medical product design field puts forward higher and higher requirements on the visual communication effect of related images. In this study, the up-sampling optimization algorithm and the threshold filtering algorithm are proposed to optimize the Laplacian graphics processing algorithm. Notably, in the threshold filtering algorithm, the Triangle algorithm is employed to address the grayscale of images pertinent to medical product design. The perception of the grayscale and binarized images by the human visual system triggers neural signals that propagate and can influence intracellular processes. When observing medical product images, neurons fire, leading to the release of neurotransmitters like glutamate. These neurotransmitters bind to receptors on cells, initiating signaling cascades such as the MAPK pathway. This pathway can affect gene expression and protein synthesis, potentially modulating cellular functions related to perception and response to the medical product design. The results show that the optimized graphical processing algorithms in this paper outperform the comparison algorithms in the CLBLAS library, and the floating-point computational values of the Laplacian algorithm are much higher than the comparison algorithms in the face of the large-scale input parameters. The Laplacian algorithm is able to accurately stitch and process the captured 2D images of cellular microtubules related to the design of the medical products in a guaranteed high efficiency (31 min), and The Laplacian algorithm was able to achieve an average subjective score of 0.856 for the visual communication of medical product design images. This graphic processing algorithm can generate images of superior perceptual quality, which holds substantial significance for augmenting the visual communication effect of medical product design images and considering the underlying cellular molecular biomechanical responses.

Traditional sports information processing methods often rely on manual observation and recording. This method is not only inefficient, but also susceptible to subjective bias, which affects the accuracy and reliability of the data. In this paper, a sports information processing and physiological condition monitoring system based on multimedia computer is constructed, which deeply integrates multimedia technology, computer technology and biomedical sensing technology. Through the integration of advanced multimedia processors and a variety of biosensors, the system can collect, process and analyze the physiological data and sports trajectory information of athletes in sports events in real time, so as to achieve comprehensive and accurate monitoring of the status of athletes. Using data compression algorithms, each byte can store two bits of data, reducing the space occupied by system operation. In terms of functional implementation, this system not only provides a user management module to ensure the security authentication of user identity, but also is equipped with a sports information data analysis module, which can provide users with scientific training guidance and optimize training plans. Experiments show that the system constructed in this article is functionally tested and all functions meet the design expectations; within 500 m, the packet loss rate of the system is 0; when the number of users reaches 1200, the response time of this system is 3.62 s; under low-intensity exercise and high-intensity exercise, the average accuracy of monitoring and early warning of users in this system is 90.44% and 95.11% respectively. The sports information processing and monitoring system can not only accurately and quickly collect and process various sports information, but also monitor and analyze the physiological data of athletes with high precision.

Intelligent decision-making in dynamic recommender systems is crucial for capturing temporal user preferences and optimizing long-term user satisfaction. Traditional recommender systems often rely on static modeling, neglecting the temporal dynamics of user-item interactions. To address this limitation, we propose a novel framework, Temporal Interpretability Graph Neural Network with Reinforcement Learning (TIGNN-RL), which integrates dynamic graph neural networks (DGNNs) and Proximal Policy Optimization (PPO) to optimize personalized recommendations. Specifically, our method models user-item interactions as dynamic graphs and utilizes temporal interpretability modules to encode both temporal features and node-specific static features. The temporal interpretability module assigns time-aware and interactions weights to user-item, enabling more time-sensitive and explainable dynamic embeddings. This TIGNN dynamic graph sequential embedding is processed by some LSTM modules to be used as the state of the deep reinforcement learning agent and states. We take a joint approach to training, earn graph embeddings that enable better PPO policy. To evaluate the proposed framework, we conduct experiments on three benchmark datasets: Last.fm 1K, MovieLens 1M, and Amazon Product Review. Results show that TIGNN-RL outperforms state-of-the-art baselines, which use GNNs for augmenting DRL-based RS, in terms of accuracy (NDCG@K) and diversity (ILD@K@K), demonstrating its effectiveness in dynamic and interpretable recommendation scenarios. In this research, some biomechanics knowledge is integrated to further enhance the understanding and application of the proposed framework in scenarios where user behavior is influenced by physical factors.

Effective sports teaching entails a deep consideration of biomechanics, which helps instructors and coaches improve athletes’ performance and reduce damage hazards. This study examines the integration of biomechanical philosophy in sports education to optimize teaching tactics for youth athletes. The primary aim is to evaluate how the biomechanical approach in sports teaching impacts the performance and skill acquisition of athletes, particularly persons exhibiting suboptimal force profiles. A randomized sample of 89 students participated in the intervention, separated into an experimental group receiving biomechanical training and a control group undergoing traditional physical education. Biomechanical analysis performance is employed to evaluate modification in performance variables, focusing on anaerobic power and sprinting mechanics. This study aims to address a specific deficiency in athletes’ force-velocity profiles, thereby enhancing their mechanical output during sprints. Paired t-tests are used in statistical analysis to assess the outcomes before and after the intervention, grouping comparisons, and performance outcomes of ANOVA. The conclusion discovered significant improvements in the experimental group, particularly in maximal horizontal force and sprint performance, with p < 0.01, indicating a strong impact of biomechanical training on athletic capabilities. The results suggest that incorporating biomechanical insights into sports teaching can significantly enhance the performance of youth athletes, making an important strategy for educators and coaches aiming to improve physical education outcomes.

The evaluation of corporate financial performance plays a critical role in driving enterprise transformation and fostering industrial development. To enhance the accuracy of financial performance evaluation, this study integrates knowledge from biomechanics and bioinformatics, exploring the application of a bio-inspired immune algorithm-optimized convolutional neural network (CNN) in financial performance evaluation. A biomechanics-based model is constructed using CNN to simulate the “mechanical response” of financial performance evaluation. By simulating the structure of biological visual systems, CNNs can effectively extract local features from input data, enabling efficient classification and recognition. During the optimization process, the biological immune algorithm adjusted hyperparameters such as the learning rate and kernel size through mechanisms of selection, reproduction, and mutation. The application of biologically inspired algorithms in deep learning effectively enhanced the model’s adaptability and robustness, providing new ideas and methods for financial performance evaluation and validating the effectiveness of bionic algorithms in complex tasks. In the experiments, a GRA-Entropy-SOM-CNN model was constructed, with initial test results showing an accuracy of 97.18% in the task. However, by introducing the biological immune algorithm to optimize the CNN, the final model achieved an accuracy of 98.5% on the test set, demonstrating significant performance improvement.

The necessity for an effective and sustainable curriculum reform has grown in importance since ice and snow sports have advanced so quickly. In order to maximize the development and delivery of ice and snow sports curriculum, this study investigates the integration of innovation and entrepreneurship education within the biomechanics framework. The study suggests using intelligent systems, like fuzzy c-means (FCM) and optimal clustering algorithms, to improve the assessment and advancement of ice and snow sports instruction from the perspective of biomechanics. This study promotes a comprehensive, data-driven approach to curriculum design that places a high priority on sustainable growth by assessing the state of information technology in ice and snow sports schools today and looking at the influence of biomechanical factors on athletic performance and injury prevention. For example, through advanced sensor technologies, coaches can precisely measure parameters like the force exerted on skis or skates, the angular velocities of joints during turns, and the balance dynamics of athletes. This data, when integrated with biomechanical principles, enables the customization of training programs. By implementing the suggested intelligent transformation model in Shenyang’s ice and snow physical education programs, the study further assesses its effectiveness and shows notable gains in both athlete performance and the instructional framework. In order to promote long-term growth and sustainability in the sector, this research combines cutting-edge technologies, biological principles, and creative teaching methodologies related to ice and snow sports education. It focuses on how biomechanical insights can drive curriculum innovation, ensuring that athletes not only master the skills but also minimize the risk of overuse injuries and enhance overall athletic efficiency in the challenging environment of ice and snow sports.

The application of neural network methods, especially convolutional neural networks (CNNs), has led to significant advances in machine translation technology. CNNs, inspired by the hierarchical organization and functional principles of biological systems, akin to how biomechanical structures adapt and respond, are able to effectively solve problems such as remote dependency and contextual nuances in language tasks, thus improving translation quality. In this study, multilayer CNN is introduced into neural machine translation (NMT), which significantly improves the BLEU score on the Chinese-English translation dataset. The optimal structure is a 6-layer CNN with 3 × 1 convolutional kernel, which performs well in context understanding. In terms of theoretical background, theories related to biological neural networks provide important insights. For example, biological neurons process information in a hierarchical structure to achieve decomposition and comprehension of complex tasks through feature extraction at different levels. CNNs mimic this biomechanically-inspired mechanism in language processing, employing convolutional layers to distill local traits and amalgamate them into comprehensive global knowledge. By exploring the successful mechanism of CNNs in language processing, this paper further reveals the transformative potential of neural hierarchical structures in computational linguistics, and opens up new paths for realizing more natural and accurate translation.

With the rapid advancements in sports science and athletic training, the integration of biomechanics and information technology has driven the development of innovative theories and practices in sports training. Traditional training methods, which lack a scientific foundation, are increasingly seen as ineffective. In contrast, the biomechanical-based sports training model proposed in this study offers a theoretical framework for precisely enhancing athletes’ performance. This model addresses several critical issues, including limited equipment adaptability, the lack of universal principles across various sports, and the challenge of tailoring training models to individual needs. To overcome these challenges, the study introduces a novel, biomechanical-based sports training model, validated through empirical research. The model is supported by a biomechanical data collection system built using multi-source sensor fusion technology, which ensures adaptability to complex training environments. This system gathers kinematic, kinetic, and electromyographic data from athletes during key activities such as double-legged downward longitudinal jumps and all-out acceleration runs. Devices like the VICON infrared camera system, a three-dimensional force measuring table, and a surface electromyography tester provide high-quality data essential for model development. Furthermore, the deep learning algorithm used in the model enhances the understanding of common principles across different sports. The model incorporates optimal designs for customized parameters to address various training needs. The empirical research employs a randomized controlled trial, dividing participants into experimental and control groups. After eight weeks of training, the model’s stability and applicability across different sports are confirmed. The experimental group’s training program is designed with a multi-phase approach, which includes injury prevention, targeted training, and recovery stretching, providing comprehensive support to athletes. The study’s findings show that the biomechanics-based sports training model significantly improves training effectiveness and fosters the integration of theoretical and practical aspects of competitive sports and sports science. This research serves as a crucial reference point for the future development of sports training models, highlighting the importance of scientific foundations in optimizing athletic performance.

Yunnan’s wetland ecosystems are essential for ecological services like water conservation and biodiversity sustenance. Analogously to biological systems in biomechanics, they are subject to diverse forces. Here, natural and anthropogenic factors act as external stimuli. Utilizing multi-source data, an evaluation index system for ecological service functions was established, similar to characterizing the biomechanical properties of an organism. Analyzing wetland dynamics and traditional plant resources is comparable to studying the structural and functional alterations of a biomechanical entity. The growth in wetland area and vegetation coverage can be regarded as a response to favorable biomechanical conditions, with the water conservation function as a crucial biomechanical attribute maintaining the system’s stability, much like a key structural element in a biological tissue. However, agricultural pollution and climate change pose challenges, acting as adverse biomechanical stressors. Agricultural pollution is like a harmful agent disrupting the normal biomechanical processes, and climate change resembles a fluctuating external force. To address these, strategies are proposed. Enhancing ecological compensation is similar to providing supplementary biomechanical energy to repair and strengthen the system. Optimizing land use structures is akin to adjusting the spatial organization of biomechanical components for enhanced efficiency. Improving management policy execution is like strengthening the regulatory biomechanical mechanisms. Through these, sustainable management of wetland resources and the enhancement of ecological service functions can be achieved, similar to restoring and optimizing the biomechanical health and functionality of a living system, ensuring the long-term viability and performance of Yunnan's wetland ecosystems in the face of complex environmental pressures.

Teachers’ mental health and general well-being have been negatively impacted in recent years by the increasing stress they experienced as a result of several difficulties in both their personal and professional lives. Teachers’ psychological stress is a crucial area for intervention since it results in burnout, decreased teaching effectiveness, and other health problems. However, there is still an abundance of research on the application of innovative technologies to track and manage teachers’ mental health. This research suggests using deep learning (DL) techniques like the Intelligent Bottlenose Dolphin-Inspired Feed Forward Neural Networks based Teacher Psychological Monitoring and Intervention Model (IDBI-FFNN-TPMIM) combined with biosensor technologies. This model offers a novel method for determining mental stress levels, identifying early indicators of burnout, and classifying emotional states as neutral, negative, or positive using biosensors like EEG and biomechanical data. Using feature extraction approaches, the model properly depicts the physical and emotional states of teachers, allowing for automatic classification and feedback for prompt interventions. According to experimental data, Biosensor-based IDBI-FFNN-TPMIM results are F1-score at 91.1%, accuracy at 93.7%, recall at 91.5%, and precision at 92.3%. While performing well in psychological monitoring and emotion recognition while achieving high prediction accuracy. These findings demonstrate how biosensor technology is employ to improve overall well-being and strengthen programs for teachers’ mental health care.

Gut microorganisms have become a hot spot of scientific research at home and abroad in recent years, in which the study of correlation between microbial community structure and intestinal diseases can provide theoretical basis for biopharmaceuticals for intestinal diseases. In this paper, we constructed in vitro simulated gastric and small intestinal digestion models, as well as large intestinal microbial fermentation models, to study the relative molecular weight and spatial structure changes of β-glucan in simulated gastric and small intestinal regions, and investigated the degradation of β-glucan in simulated large intestinal regions as well as its effects on intestinal microorganisms. In addition to the biochemical and metabolic aspects, integrating biomechanical principles into this research can enhance our understanding of how gut microbes interact with the host’s physiological environment. For instance, the biomechanical properties of the gut—such as motility, peristalsis, and the mechanical forces exerted on microbial populations—can influence the distribution and activity of gut microorganisms. Understanding these biomechanical factors may reveal how they affect the degradation of β-glucan and the overall microbial community structure. Secondly, fecal microorganisms from different batches of mice and different individuals of human volunteers were collected as inoculum for fermentation of β-glucan, to analyze the main microorganisms that stably responded to β-glucan in different batches of fermentation experiments as well as in gut microorganisms from different individuals and to further investigate the metabolic changes, metabolic pathways as well as the biomarkers of β-glucan in the simulated large intestine. L. murinus Mic06, L. murinus Mic07, L. murinus Mic08, and L. murinus Mic094 were validated to be able to utilize β-glucan and produce a small amount of reducing sugars in all four species of Lactobacillus intestinalis in mice, and there was no significant difference in the ability to utilize them; All nine species of human enterobacteriophages were able to utilize β-glucan and produce reducing sugars, with B. xylanisolvens Bac02 and B. koreensis Bac08 having a significantly greater ability to utilize β-glucan. This study contributes to a deeper understanding of gut disease-associated flora and provides strong support for the use of the gut microbiome for multidisease classification. Additionally, considering biomechanical aspects may lead to novel insights into the interactions between gut microbes and host physiology, enhancing the development of targeted biopharmaceuticals.

The application of games in sports not only brings new development to sports, but also brings new requirements to sports. To maintain and enhance students’ learning motivation and interest, more effective individualized teaching is needed. This study focuses on students’ individualized knowledge structure, tracking the differences in sports skills among different types of students, and designing a sports game model based on sports games. A Bayesian network model was used to model learners’ knowledge and establish an adaptive badminton competition mode. Then, a new feature intersection method is studied, and a method for deep knowledge tracking is established using feature embedding and attention mechanisms. Finally, on this basis, this method is combined with adaptive learning methods to establish a badminton game model based on adaptive learning and improved deep knowledge tracking. Additionally, this study explores the biological principles underlying sports skill learning. When students learn badminton skills. When students learn badminton skills, the body’s proprioceptive system constantly provides feedback. This biological feedback is vital as it helps students adjust their movements unconsciously. Motion capture technology can capture the kinematic data of students’ badminton movements, such as joint angles and limb velocities. By integrating this data with the biological feedback, we can optimize learning outcomes by enabling more precise identification of skill deficiencies and more effective remediation. The experiment shows that the acceleration Z is the maximum, approximately 100. The acceleration of X is the smallest, approximately between −200 and 200. The most unstable is the acceleration Y. After positive compensation, the value of the adaptive quantization parameter cascade algorithm increases. And the quantized adaptive quantization parameter cascade algorithm value does not have a significant impact on the evaluation of the reconstructed image. The average values of each scale and sub dimension are above 0.70, and the constituent reliability values are above 0.90, indicating that the internal quality of each scale is good. And the internal consistency between questions is also good, all of which have passed the validity test. The survey method used in this experiment has strong practicality and can effectively achieve game design, which has great practical value in teaching practice.

Injury detection plays a critical role in minimizing athlete downtime, ensuring safety, optimizing performance, and preventing long-term physical or mental consequences. In college sports, effective injury prevention and detection strategies enhance athlete safety, support peak performance, reduce healthcare costs, and contribute to sustainable athletic development programs. This research evaluates the application of biosensor technology in identifying injury risks, monitoring physiological metrics, and enhancing preventive strategies in college sports training to improve athlete performance and safety. A novel model, Egret Swarm Search-driven Scalable Deep Convolutional Neural Network (ESS-SDCNN), addresses the limitations of traditional approaches by combining SDCNNs with ESS algorithm for optimized feature selection, hyper parameter tuning, and real-time adaptability. Suitable data for injury detection and prevention include real-time physiological readings, motion sensor data, activity patterns, and injury records, with a focus on wearable technology. The Z-score normalization ensures consistent feature scaling. Independent Component Analysis (ICA) is used to extract hidden components from sensor data for improved feature representation. The SDCNN efficiently processes high-dimensional biosensor data, extracting spatial-temporal patterns related to injuries. The ESS algorithm further optimizes feature selection and hyper parameters, enhancing model accuracy, robustness, and adaptability for real-time applications. Results demonstrate that the hybrid ESS-SDCNN model significantly improves injury detection accuracy, enables faster convergence, and provides real-time monitoring and prevention insights. This approach enhances athlete safety, supports injury prevention, and fosters better performance outcomes in college sports training programs.

This work explores the biomechanical characteristics of key actions in fencing techniques using motion capture and biomechanical analysis technology, aiming to provide scientific evidence for athlete training and performance. The work combines eight infrared high-speed cameras with the Delsys surface Electromyography system for synchronized analysis, making an innovative contribution to the biomechanical research of fencing techniques. This technological combination allows for more precise tracking of an athlete’s three-dimensional movement trajectories and muscle activation, and offers new perspectives and more accurate guidance for training. The results are as follows. (1) During the forward lunge step, the integrated electromyographic activity of the deltoid muscle significantly increases (152.55 µV·s, p = 0.045), indicating a higher demand for arm stability in this movement. There are no significant differences in the activation levels of the biceps brachii and triceps brachii. The activation of the forearm muscles, specifically the extensor carpi radialis longus and extensor carpi radialis brevis, is significantly enhanced, at 81.61 µV·s (p = 0.047) and 98.72 µV·s (p = 0.049), respectively. For the lower limbs, the activation of the tibialis anterior muscle significantly increases (110.34 µV·s, p = 0.000). The activation of the gastrocnemius medialis and gastrocnemius lateralis also significantly enhances, with values of 53.22 µV·s (p = 0.001) and 35.75 µV·s (p = 0.000), respectively. The contribution of the deltoid muscle significantly increases to 31.2%, while the tibialis anterior muscle contribution increases to 26.5%. (2) The work also compares muscle activity, movement characteristics, and biomechanical parameters across athletes of different skill levels (beginner, intermediate, and advanced). The results show that the beginner group has the highest electromyography activity intensity (45.2 ± 5.1 µV), while the advanced group has the lowest (32.5 ± 3.8 µV). The movement trajectory stability is 12.3 ± 2.1 mm/s for the beginner group and 6.5 ± 1.2 mm/s for the advanced group. These results suggest that advanced athletes exhibit higher training effects in muscle activation efficiency and energy economy. These findings provide important theoretical support for optimizing fencing training methods and improving athletic performance.