Aiming at the variable speed condition, the bearing vibration signal is prone to signal feature aliasing, frequency shift, signal truncation and noise pollution, a fault classification model combining angular resampling, principal component analysis (PCA) and extreme gradient boosting tree (XGBoost) is proposed. Secondly, the time-frequency feature parameters are extracted by principal component analysis (PCA), and the main elements with total contribution greater than 95% are selected as input samples of XGBoost model; finally, the main parameters of XGBoost are tuned by grid search method, and the model is trained by dividing the training set and the test set to verify the accuracy of its fault classification. The results show that the accuracy of fault diagnosis is 96.44%, the running time is shortened by 27.24 s compared with that of the data without dimensionality reduction, and the diagnosis effect after angle resampling is obviously better than that of the diagnosis effect without angle resampling, and the fault recognition rate is improved by more than 7%, which proves that the proposed method can make diagnosis more quickly and accurately.