Automatic Detection and Early Warning System of Students' Abnormal Behavior Based on Pattern Recognition

This paper proposes an automatic detection and early warning system for student abnormal behavior based on pattern recognition, aiming to timely identify and alert abnormal behaviors by real-time monitoring and analyzing student behaviors, thereby ensuring campus safety and improving teaching quality. The system adopts a hierarchical architecture design, including the data acquisition layer, data preprocessing layer, pattern recognition and anomaly detection layer, as well as the early warning and intervention layer. By integrating deep learning algorithms, especially the hybrid model of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), and introducing the attention mechanism, the system can efficiently and accurately recognize abnormal behavior patterns of students during their school time. Experimental analysis shows that the system outperforms existing methods in various performance metrics such as accuracy, recall, and F1 score, effectively assisting educators and parents in decision-making support. Additionally, the system also takes into account challenges such as data privacy protection and algorithm generalization ability to ensure its effectiveness and reliability in practical applications.