Research on Early Warning of Entrepreneurial Risks and Enhancement of Employment Competitiveness for College Students Driven by Big Data Algorithm Model and Application Validation
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Abstract
Accurate prediction of entrepreneurial risks and enhancement of employment competitiveness are essential for improving student career development outcomes in dynamic labor markets. This study proposes an integrated framework combining multi-source data fusion, time-series risk assessment, and intelligent recommendation algorithms. Student digital profiles are first constructed through heterogeneous campus data integration. A temporal early-warning model is then developed to identify entrepreneurial risks dynamically, while graph neural networks and reinforcement learning are employed to generate personalized employment capability enhancement pathways. Experimental results indicate that the proposed model achieves an entrepreneurial risk prediction accuracy of 88.7% and provides effective early-warning capability. Real-world validation further demonstrates improvements in entrepreneurial survival rates, employment matching, and career competitiveness. The study offers a data-driven solution for talent development and provides methodological references for predictive analytics, intelligent recommendation systems, and information fusion technologies.
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References
J. Zhang, J. Huang, and S. Ye, “The impact of career adaptability on college students’ entrepreneurial intentions: A moderated mediation effect of entrepreneurial self-efficacy and gender,” Current Psychology, vol. 43, no. 5, pp. 4638–4653, 2024, doi: 10.1007/s12144-023-04632-y.
A. Hassan, I. Anwar, A. Saleem, et al., “Nexus between entrepreneurship education, motivations, and intention among Indian university students: the role of psychological and contextual factors,” Industry and Higher Education, vol. 36, no. 5, pp. 539–555, 2022, doi: 10.1177/09504222211053262.
T. Moorthy and S. Sahid, “The influence of digital marketing literacy on entrepreneurship behavior among public university students in Malaysia,” International Journal of Academic Research in Business and Social Sciences, vol. 12, no. 1, pp. 548–568, 2022, doi: 10.6007/IJARBSS/v12-i1/11837.
C. Donaldson, J. Villagrasa, and H. Neck, “The impact of an entrepreneurial ecosystem on student entrepreneurship financing: a signaling perspective,” Venture Capital, vol. 26, no. 4, pp. 431–466, 2024, doi: 10.1080/13691066.2023.2221392.
A. Brzozowska, S. Kubiciel-Lodzińska, K. Widera, et al., “Risk-taking and entrepreneurial intentions among native and international students: Exploring tendencies,” Journal of Economics and Management, vol. 47, no. 1, pp. 602–632, 2025, doi: 10.22367/jem.2025.47.22.
B. Hernández N, L. Vázquez M Y, G. Caballero E, et al., “A new method to assess entrepreneurship competence in university students using based on plithogenic numbers and SWOT analysis,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 21, no. 3, pp. 280–292, 2021, doi: 10.5391/IJFIS.2021.21.3.280.
Q. Tang, Y. Zhao, Y. Wei, et al., “Research on the Mental Health of College Students Based on Fuzzy Clustering Algorithm,” Security and Communication Network, vol. 2021, no. 3, pp. 1–8, 2021, doi: 10.1155/2021/3960559.
T. Qian, J. Bian, and J. Chen, “The status quo, causes, and countermeasures of employment difficulties faced by college graduates in China,” Labor History, vol. 65, no. 4, pp. 528–543, 2024, doi: 10.1080/0023656X.2023.2283065.
W. Wang, D. Qiu, X. Chen, et al., “An empirical study on the evaluation system of innovation and entrepreneurship education in applied universities,” Computer Applications in Engineering Education, vol. 31, no. 1, pp. 100–116, 2023, doi: 10.1002/cae.22573.
Q. Gnoh H, H. Keoy K, J. Iqbal, et al., “Enhancing business sustainability through technology-enabled AI: Forecasting student data and comparing prediction models for higher education institutions (HEIs),” PaperAsia, vol. 40, no. 2b, pp. 48–58, 2024, doi: 10.59953/PAPERASIA.V40I2B.86.