Advanced AI-Driven Predictive Analytics for Efficient Student Management in Higher Vocational Colleges

Abstract

Recent advances in artificial intelligence have created new opportunities for efficient student management in higher vocational colleges, especially in campuses where academic, behavioral, psychological, and employment data are generated through digital platforms and wireless information systems. Based on empirical data from 2,486 enrolled students at a higher vocational college in Southwest China, this study constructs a multi-task intelligent prediction system integrating Random Forest, XGBoost, BiLSTM, and Stacking ensemble learning. The system supports four management scenarios: academic early warning, mental health risk identification, employability prediction, and comprehensive administrative decision support. Feature engineering, cross-validation, SMOTE balancing, and SHAP interpretability analysis are introduced to improve predictive accuracy and practical transparency. The Stacking ensemble model achieves the best performance across tasks, with an AUC-ROC of 0.946 in academic early warning and strong recall in high-risk mental health identification. From an engineering perspective, the framework is relevant to intelligent campuses supported by wireless electromagnetic communication, antenna-enabled terminals, and secure data transmission. The findings provide a localized practical reference for precision-oriented, data-driven, and ethically controlled student management in higher vocational education.