Aiming at the low efficiency of fault diagnosis caused by the diversity and uncertainty of fault occurrence in the ZPW-2000A jointless track circuit, this paper proposes a deep convolutional neural network (DCNN)-based fault diagnosis method for jointless track circuit from the perspective of fault feature extraction and fault classification. Twelve rail circuit fault states are summarized through fault analysis, and the monitoring data of rail circuits under different fault states are standardized as inputs to the DCNN model. The model adopts the convolution-pooling structure to extract the key features of the rail circuit and filter out the redundant features. The back propagation neural network (BPNN) is used as the fully connected layer of the model and combined with the Softmax function for fault classification. The model structure is optimized by the k-fold cross-validation method to determine the best model. The experimental results show that the track circuit fault diagnosis model with a four-layer convolution-pooling layer structure achieves 98.48% in diagnosis accuracy, which is 6.06%, 6.06%, 3.33%, and 2.27% higher than the optimal models of long short-term memory (LSTM), deep feedforward network (DFN), bidirectional long and short-term memory (BiLSTM), and the combination of CNN-LSTM, respectively. Additionally, the training convergence speed is about 1 250, 4 250, 1 250, and 1 450 times faster, respectively, with less loss fluctuation during training. This study improves the efficiency of fault diagnosis of rail circuits and provides a new, effective method for the task of fault diagnosis of rail circuits.