Vol. 2 No. 9 (2025)

This issue presents an innovative study on intelligent evaluation systems for higher vocational internship quality based on multi-source data fusion. The research addresses critical challenges in traditional internship assessment, including subjectivity, data fragmentation, and delayed feedback. By integrating student logs, enterprise evaluations, teacher feedback, and attendance records through advanced technologies such as K-means clustering and Natural Language Processing, the system enables personalized performance analysis and real-time monitoring. Empirical testing with 300 interns demonstrates significant improvements in internship-position matching and provides actionable insights for educational institutions. This work offers a reusable technical paradigm for the digital transformation of vocational education assessment systems.