Research on College Student Behavior Analysis and Campus Safety Risk Early Warning System Based on Deep Learning and Big Data Analysis

Campus safety management is an important issue that colleges and universities need to address urgently at present. The traditional management approach is difficult to cope with the demand for safety risk identification under the complex behavioral patterns of students due to the lack of dynamic analysis and prediction capabilities. To this end, this study proposes a campus safety risk early warning system based on deep learning and big data analysis, which takes recurrent neural network (RNN) as the core and realizes dynamic prediction and accurate classification of potential safety risks of students by modeling time series data from multiple sources. The system generates personalized risk scores and classification labels through feature extraction and time-series modeling, and combines with a rule engine to develop targeted intervention strategies. Experimental results show that the system achieves an F1 score of 84% on the test set, which is significantly better than the 70% of the traditional method. Through intelligent interventions, the percentage of high-risk students decreased by 42%, the incidence of mental health problems decreased by 35%, and campus safety incidents decreased by 33%, which verified the practicality of the system and the effectiveness of interventions. The study further shows that the deep learning-based risk prediction and intervention system can effectively improve the intelligent level of campus safety management. The innovation of this study is to combine deep learning technology with the needs of education management to construct an intelligent, data-driven campus safety management framework, which not only improves the efficiency of risk identification and intervention, but also provides a feasible path for the digital transformation of education management.