To solve the problem that it is difficult to distinguish bearing fault state under complex working conditions,a bearing fault diagnosis method of multi-domain feature fusion based on principal component analysis was proposed.The vibration acceleration signals was collected,and the new dimensionless features in time domain,amplitude spectrum features in frequency domain and empirical mode decomposition features in time frequency domain were extracted to fully described the bearing state.The extracted features were fused and reduced in dimension by the principal component analysis method,it can effectively reduce the complexity of diagnostic models and the difficulty of data analysis.Finally,a suitable convolutional neural network was selected to classify,the verification was performed by the petrochemical unit fault diagnosis experimental platform.The results show that the multi-domain fusion feature diagnosis is better than the single domain feature diagnosis,the convolutional neural network classification model has higher diagnostic accuracy than other classical classification models,the diagnostic accuracy of the fusion diagnosis classification method reaches 86%.