Because of the complex friction pair components and many wear particle energy spectrum monitoring elements in aero-engine lubrication system,it is difficult to accurately identify the wear parts by manual experience.A wear part identification method of aero-engine lubrication system based on deep learning was proposed.In which a one-dimensional convolution residual network model was established by using one-dimensional convolution kernel as the calculation unit.Taking the energy spectrum analysis data of the wear particles in the aero-engine oil as the input,using the one-dimensional convolution residual network model,the feature extraction of energy spectrum data and the location and recognition of aeroengine wear parts were realized.The effectiveness of the method was verified by the measured energy spectrum data of the wear particles in an aeroengine oil,and compared with resnet18,resnet34,CNN and other network models.The results show that the recognition accuracy of aeroengine wear parts by the proposed method is more than 95%.In order to verify the robustness and generalization ability of the model,the oxygen data and noise data were tested respectively on the basis of the real aero-engine energy spectrum data,which further verified the effectiveness of the model in wear location and identification and the feasibility of practical application.