Abstract: In response to the low accuracy of fault diagnosis caused by the diversity and complexity of ZPW-2000R uninsulated track circuits, this paper proposed a fault diagnosis method based on the SSAE-DNN model by combining Stacked Sparse Autoencoder (SSAE) and Deep Neural Networks (DNN) from two aspects of fault feature extraction and feature classification. The paper used SSAE to unsupervised dimensionality reduction and feature extraction of fault data, obtained the optimal network parameters to mine different fault feature information of uninsulated track circuits. The feature samples extracted by SSAE were imported into DNN to obtain the SSAE-DNN model for fault classification and recognition of uninsulated track circuits. The experimental results show that the model reduces the dimensionality of fault data, reduces fault diagnosis time, and obtains deep features of fault data, has a high diagnostic accuracy for faults in ZPW-2000R uninsulated track circuits, with only a few faults experiencing misjudgment. The effectiveness and superiority of this method were further verified through comparative experiments with backpropagation (BP) neural networks, convolutional neural networks (CNN), and support vector machines (SVM).