Construction of a Dynamic Prediction Model for Student Management Based on Deep Belief Networks

The dynamic nature of academic environments necessitates predictive models capable of addressing temporal, spatial, and categorical complexities inherent in student management. Aligning with the thematic scope of “Frontiers in Computer Science”, this research introduces an innovative framework based on Deep Belief Networks (DBNs) to enhance the accuracy and adaptability of student outcome forecasting. Traditional methods often struggle with integrating heterogeneous data sources, capturing nonlinear relationships, and adapting to fluctuating academic conditions, leading to limited scalability and robustness. To overcome these limitations, we propose the Hierarchical Student Outcome Predictor (HSOP) integrated with the Dynamic Academic Optimization Strategy (DAOS). HSOP leverages temporal–spatial reasoning and hierarchical feature encoding to model complex dependencies in student data. Concurrently, DAOS dynamically adjusts model parameters and regularization strategies using real-time data inputs, ensuring stability and relevance across diverse academic scenarios. Experimental validation demonstrates the superiority of our approach in predicting enrollment trends, academic performance, and resource allocation, significantly outperforming traditional models. This research contributes to advancing AI-based solutions in educational management by addressing scalability, adaptability, and predictive accuracy.