Construction of model for extracting and identifying behavioral characteristics of college students with psychological abnormalities based on education big data mining and analysis

With the increasingly prominent mental health issues among college students, how to identify students with psychological abnormalities through behavioral data has become an urgent research problem to be solved. A Hybrid Model for Psychological Abnormal Student Behavior Identification (HMPABI) based on educational big data is proposed. By combining multidimensional behavioral characteristics and psychological assessment data of students during their school years, an efficient and accurate identification model for students with psychological abnormalities is constructed through clustering, oversampling techniques, and a mixed classification strategy of logistic regression and support vector machine. The study takes the Urumqi University Student Campus Behavior Dataset and the Adolescent Mental Health and Behavior Dataset for experimental verification. On the UUS-CBD dataset, the average absolute error of the HMPABI model was lower than that of other comparison models in all testing time windows, exhibiting higher prediction accuracy and stability, with a maximum error value of 0.28. In contrast, the errors of the other two models reached 0.45 and 0.42 at the maximum time window (45 min), respectively. The HMPBAI model can fully explore the potential information of students’ behavioral characteristics. By integrating different types of data, it can more accurately predict students with psychological abnormalities. This study provides a new technological path for monitoring and intervening in the mental health of college students and has high practical application value.