Study on the construction and dynamic monitoring of crisis early warning index system in college student management

The management of college students is faced with academic, psychological, economic and security risks, and it is difficult for traditional management methods to achieve accurate early warning and dynamic monitoring. In order to improve the management efficiency, this study constructs a set of college students' crisis early warning index system based on multi-source data, and puts forward a dynamic monitoring mechanism. Firstly, based on educational management theory, analytic hierarchy process (AHP) and big data analysis technology, this paper constructs an early warning index system covering four core dimensions: academic, psychological, economic and security, and sets the early warning level and intervention measures through data analysis. Relying on machine learning and multi-source data fusion technology, a dynamic monitoring system is constructed to realize real-time tracking of students' abnormal behavior. The empirical study selects the student data of a university, and verifies it by descriptive statistics, correlation analysis and machine learning modeling. The results show that the system can effectively identify high-risk students and improve management accuracy. The research emphasizes that the early warning mechanism needs to combine humanistic care, comprehensively use technical means and personalized counseling to optimize the management mode of college students and promote their all-round development.