The failure of reciprocating compressor happens frequently for the complex structure and rich excitation sources. Due to the difficulty of designing fault features and relying on experience, the traditional methods have no strong diagnosis ability. The intelligent diagnosis method based on convolutional neural networks ( CNN) can realize end-to-end fault diagnosis without feature extraction. However, there are some problems such as inaccurate extraction of fault features, large number of model parameters and long training time. Therefore, a fault diagnosis method of reciprocating compressor based on PyTorch deep learning framework MPMRNet (multipleprocesses-mini-ResNet) is proposed. In this method, multiple processes are used to load data, ResNet50 is taken as the basic network framework and its depth and width are reduced. Adam and StepLR strategy are used to optimize the network and adjust the learning rate, respectively. And time-frequency images of vibration signals are processed automatically to deeply mine and evaluate sensitive features. Multiple comparison experiments show that this method significantly shortens the training time of the model, reduces the number of model weight parameters from 94. 1 to 0. 58 M, the complexity of the model from 4. 11 to 0. 21 G, and the memory occupancy rate from 37. 08% to 10. 92%, and the fault diagnosis accuracy is up to 98. 28%, the diagnostic ability of the model is obviously improved.