An explainable hybrid deep learning framework for multi-source student employment prediction

Accurately predicting student employment outcomes remains a significant challenge in educational data mining, particularly given the increasing diversity of student backgrounds and the dynamic nature of labor market demands. This study proposes an explainable hybrid deep learning framework that integrates multi-source heterogeneous data, including academic records, demographic profiles, financial attributes, and engagement in scientific or organizational activities, to perform multi-class employment prediction. The framework employs recursive feature elimination for precise feature selection, a bi-directional long short-term memory network with attention mechanisms to capture temporal academic patterns, and a tree-structured Parzen estimator-optimized XGBoost classifier to model complex feature interactions. To enhance model interpretability, SHAP values are utilized to quantify the contribution of each feature to the final prediction. Extensive experiments on a real-world vocational college dataset demonstrate that the proposed model consistently surpasses competitive baselines across multiple evaluation metrics, including accuracy, macro-F1, AUC, and Cohen's kappa. The results confirm the framework's capability to effectively leverage both longitudinal educational trajectories and static characteristics for accurate and interpretable prediction of employment outcomes.