In the research of automatic classification of rolling bearing faults,the use of traditional machine learning methods requires manual extraction of features,so the extracted features are not sufficient and the adaptability is not strong.In view of the above problems,a single-channel rolling bearing fault classification model combined one dimensional convolutional neural network(1D CNN) with the XGBoost algorithm was proposed.This model combined the advantages of 1D CNN and XGBoost,the collected bearing vibration signals were divided into data sets;the training set was used to train the 1D CNN,and the 1D CNN model was saved and used to realize the automatic extraction of bearing data features;the extracted feature data set was substituted into the XGBoost algorithm for training and classification.In order to verify the effectiveness of this model,the data provided by Case Western Reserve University Bearing Data Center was used to compare the 1D CNN model,XGBoost model and 1D CNN-XGBoost model;to verify the generalization of 1D CNN-XGBoost,an another rolling bearing data set was used in the experiment.The results show that the classification accuracy of the 1D CNN-XGBoost model is higher and it is an effective bearing fault classification model with good classification performance and generalization.