Design and implementation of a big data-driven intelligent early warning system for psychological crises among vocational college students

Abstract

A big data-driven intelligent early warning system for psychological crises among vocational college students is presented in this study, addressing the urgent need for precise, real-time mental health risk identification in complex educational environments. Leveraging a cloud-native Lambda architecture, the system integrates high-frequency heterogeneous behavioral data from campus sources and applies advanced feature engineering to construct a unified, robust data foundation. By combining gradient boosting machines and deep neural network models to improve recognition accuracy and support dynamic adaptation to diverse individual patterns. The core components of the system include alert propagation based on RESTful API for automatic adjustment of multi-level alert sensitivity thresholds; additionally, distributed feature storage to ensure model consistency between offline training and real-time inference; and distributed use of feature storage. Kubernetes orchestration deployment allows the system to quickly allocate resources and handle event streams under high concurrency, while also enabling system scalability. Pilot data from empirical evaluation agencies indicate that the intervention has significantly improved the lead time for actionable interventions, the false positive rate, and the accuracy of early warnings. Results confirm the practical effectiveness of explainable artificial intelligence in augmenting campus crisis management workflows and provide a rigorous baseline for the future development of integrated mental health support platforms.