Research on the Identification and Early Warning Model of College Students’ Cybercrime Behavior Characteristics Based on Data Mining

Abstract

To overcome the limitation that traditional models fail to capture deep correlations in college students’ online behaviors, this paper proposes an early-warning model for cybercrime behavior characteristics based on multi-source data mining and deep graph learning. First, internet browsing logs, campus-card consumption records, dormitory access-control data, and psychological assessment data are integrated to construct a behavioral feature database. Second, a dynamic community graph is built using spatial co-occurrence, consumption similarity, and course-overlap relationships. Third, a heterogeneous graph attention network is used to extract behavioral transmission characteristics in student social networks. Finally, a risk-identification module is constructed for early warning. Experiments on anonymized campus data show that the model achieves 91.3% accuracy and 90.5% recall, with an early-warning lead time of 6.5 days compared with LSTM-based methods. The results verify the effectiveness of the proposed model in deep feature mining, risk recognition, and proactive cybercrime prevention in campus network governance.