To address the need for enhanced educational quality and effective student management, this study introduces a novel model for predicting student engagement and delivering personalized recommendations by integrating a GRU-Attention network with an L-DMF recommendation algorithm. Our approach employs a GRU-Attention network to analyze student behavior data and accurately predict engagement levels. The attention mechanism enhances the model’s ability to prioritize significant features, resulting in an impressive prediction accuracy of 98.15%, surpassing traditional classification methods such as decision trees, support vector machines, and random forests. In addition, the author proposes an L-DMF-based recommendation system that utilizes student behavior data to generate tailored suggestions. The model’s performance was compared with leading recommendation algorithms, including LibFM, KGCN, and DRER. The results demonstrate that our approach provides more accurate and contextually relevant recommendations. By effectively incorporating both spatial and temporal features of student behavior, our model achieves superior results in both engagement prediction and recommendation tasks. Overall, the dual focus on precise engagement forecasting and personalized recommendation highlights the model’s efficacy in enhancing educational management and student support.

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