Integrating neural network and multimedia technologies to enhance college students’ career development
Abstract
Combining neural network technologies and computational techniques, this research establishes a career development promotion system based on a multi-modal neural network. It reveals that computer simulation technology and multimedia have positive intervention effects on college students’ career decision-making behaviors, similar to how biomolecular interactions regulate biological processes. This technology ensures scientific rigor, objectivity, and authenticity. A knowledge fusion algorithm, built on attributes and rules within the Hadoop platform and MapReduce parallel computing framework, facilitates effective data integration. Additionally, inspired by the regulatory mechanisms in biomolecular systems, a neural network-based algorithm, utilizing gradient descent, is applied to cultural learning, augmented by feedback analysis to assess students’ psychological changes, posture, and response dynamics during the learning process. To further optimize the career development framework, an Evolutionary Algorithm (EA) is used to enhance the performance of neural networks. Numerical simulations demonstrate the robustness of the proposed algorithm, achieving high accuracy (0.981), recall rate (1.0), and F-measure (0.997) in similarity computations. These results are particularly notable when biomechanic metrics, such as gesture and posture tracking, are integrated with linguistic data, such as spelling and vocabulary. The findings underscore that incorporating neural network insights into multimedia teaching methodologies can significantly enhance psychological motivation, behavioral adaptability, and engagement in college students, fostering improved educational outcomes and advancing interdisciplinary innovation in neural networks. It effectively enhances the internal driving force of “technology empowering psychological development” in the career planning system and provides a cognitive computing and biomechanic perspective for the construction of the smart education ecosystem.
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