Student Mental Health Risk Prediction Based on Apriori Algorithm in the Context of Big Data

This study takes college students' mental health as the research object, combined with big data technology, and uses association rule mining algorithm Apriori to deeply understand the characteristics and trends of students' mental health. A large scale mental health data was collected through the psychological evaluation system, and a psychological association analysis system was constructed. This paper uses data mining technology to identify the correlation rule in the field of college students' mental health, and establishes the mental health risk prediction model by applying the association rule weight. The results showed the correlation between learning time, social activities and sleep time, and established the corresponding model for risk prediction. Scientific support for personalized interventions and prevention measures, as well as new perspectives and tools for the study and prediction of mental health problems, underscore the importance of data-driven approaches in the field of student mental health.