To tackle the problem that existing single algorithm models have poor generalization ability in performance prediction, this study proposes a Stacking ensemble learning model based on multi-algorithm fusion for the prediction of students’ performance in blended teaching. The model uses polynomial naive Bayes, AdaBoost and Gradient boosting as primary learners and logistic regression as secondary learners to form a two-level fusion framework. The model is verified by the learning behavior data generated in the process of blended teaching. Experimental results show that the classification and prediction accuracy of the Stacking ensemble learning model on the test set reaches 76%, which is 5%, 6%, 9% and 6% higher than that of the four single algorithm models of polynomial naive Bayes, AdaBoost, Gradient boosting and logistic regression, respectively. Compared with these single algorithm models, the Stacking ensemble learning model has strong generalization ability, which can better predict students’ performance and provide a reference for the learning warning of blended teaching.