In the context of digital transformation, establishing an efficient and precise employment and entrepreneurship service system for college students has become a crucial challenge in higher education. This study systematically explores the application logic and implementation pathways of big data technology in this field, leveraging its multidimensional empowerment capabilities. The research first identifies structural shortcomings in traditional service systems regarding data integration, demand matching, and dynamic tracking, revealing how existing models struggle to meet personalized development needs due to information asymmetry and delayed service responses. Building on the core advantages of big data technology, the study constructs a closed-loop service model of "data collection-intelligent analysis-precise services-effectiveness feedback". It specifically demonstrates the application of data mining algorithms in employment trend prediction, entrepreneurial opportunity identification, and competency gap diagnosis, while explaining how user profiling technology enables multi-dimensional precision matching between individuals, positions, and resources. The research shows that big data-driven service systems can significantly enhance resource allocation efficiency, improve service foresight and relevance, and provide new paradigms for addressing challenges in college student employment and entrepreneurship. This study offers theoretical references and practical pathways for higher education institutions to deepen employment and entrepreneurship reforms and for governments to optimize public service provision.
Yunliang Wang (Tue,) studied this question.