Research on Psychological Crisis Early Warning System for College Students Integrating Big Data and Machine Learning
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Abstract
This study develops a psychological crisis early-warning system for college students by integrating big data technologies and machine learning methods. A multi-source data acquisition framework is established to collect and process behavioral, academic, social, and psychological information, while a Dual-Channel CNN-FM Feature Fusion Model (DCCFM-FFM) is employed to enhance crisis prediction accuracy through heterogeneous feature extraction and fusion. The system incorporates distributed storage, real-time data processing, edge computing, and intelligent risk assessment mechanisms to support dynamic monitoring and timely intervention. Experimental results demonstrate that the proposed framework achieves high predictive performance, with accuracy, recall, and F1-score significantly outperforming conventional early-warning approaches. The architecture is particularly suitable for communication-intensive environments supported by wireless communication infrastructures and antenna-enabled information transmission networks, where reliable data acquisition, low-latency processing, and secure information exchange are essential for intelligent decision-making. The proposed system provides an effective engineering solution for large-scale psychological risk monitoring and contributes to the development of intelligent health-management and communication-oriented data analysis platforms.
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