It is not completely understood fatigue strength at this time due to its complex formation mechanism. Therefore, in order to address this issue, machine learning has been used to examine the important factors involved in predicting fatigue strength. In this study, a hybrid model was proposed based on the modified bagging method by combining XGBoost and LightGBM, in which the hyperparameters of the models were optimized by a grey wolf algorithm. Moreover, an interpretable method, referred to as Shapley additive explanations (SHAP), was introduced to explain the fatigue strength predictions made by ML models. The SHAP values were calculated, and feature importance of fatigue strength by XGBoost, LightGBM and the hybrid model was discussed. The final results demonstrated that the SHAP method had major potential for interpreting fatigue strength predictions, which would provide constructive guidance for the development of antifatigue steel material in the future.