Data-driven techniques improve the quality of talent training comprehensively for university by discovering potential academic problems and proposing solutions. We propose an interpretable prediction method for university student academic crisis warning, which consists of K-prototype-based student portrait construction and Catboost–SHAP-based academic achievement prediction. The academic crisis warning experiment is carried out on desensitization multi-source student data of a university. The experimental results show that the proposed method has significant advantages over common machine learning algorithms. In terms of achievement prediction, mean square error (MSE) reaches 24.976, mean absolute error (MAE) reaches 3.551, coefficient of determination (\(R^{2}\)) reaches 80.3%. The student portrait and Catboost–SHAP method are used for visual analysis of the academic achievement factors, which provide intuitive decision support and guidance assistance for education administrators.

数据驱动技术通过发现潜在的学术问题并提出解决方案，全面提高了大学人才培养的质量。我们提出了一种用于大学生学术危机预警的可解释的预测方法，该方法包括基于K原型的学生肖像构造和基于Catboost–SHAP的学术成就预测。对大学的脱敏多源学生数据进行了学术危机预警实验。实验结果表明，与普通的机器学习算法相比，该方法具有明显的优势。在成就预测方面，均方误差（MSE）达到24.976，平均绝对误差（MAE）达到3.551，确定系数（\（R ^ {2} \））达到80.3％。学生画像和Catboost–SHAP方法用于对学业成就因素进行视觉分析，从而为教育管理者提供直观的决策支持和指导帮助。