By comprehensively considering the imbalance and sparsity of data in blended learning grade classification and prediction, this study proposes a blended learning grade classification and prediction model, namely, SMOTE-XGBoost-FM. Firstly, an equalization data set is sampled by SMOTE. In order to solve the problem of data sparsity, XGBoost is used to perform feature overlap on the sampled data, and then the leaf nodes of the generated tree are processed by one-hot encoding to generate high-order feature data. Finally, the data are input into a factorization machine (FM) for iterative training to obtain the optimal model. The experimental results show that the SMOTE-XGBoost-FM model achieves an accuracy of 92.7% in blended learning grade classification and prediction, which is 5.7% and 11.7% higher than that of single XGBoost and FM models, respectively. Therefore, it can effectively classify and predict students’ learning effects and provide a reference for improving teaching efficiency.