Prediction and identification of faults in track circuit are crucial for improving the safety and efficiency of railway transportation. However, the task of track circuit fault prediction through deep learning methods facing significant challenges due to the absence of reliable data. In this paper, a novel heterogeneous transfer learning method is proposed, aiming to reduce track circuit data reliance in model training by using publicly available datasets in other similar fields. An index describing the data distribution based on autoencoder feature extraction and maximum mean discrepancy is used to demonstrate the transferability between heterogeneous data firstly. Then a heterogeneous transfer learning method is constructed to accelerate track circuit fault prediction model training. Furthermore, the resulting deep learning model is compared to existing fault prediction methods. Finally, by adjusting the degree of involvement of transfer learning throughout model training, this paper comprehensively examines its effect on model training process. The simulation experimental results show that the proposed method can transfer useful knowledge in other similar fields for tasks in track circuit fault prediction, and the resulting model can correctly classify over 99% on the test dataset while reducing the amount of required track circuit data to 10% of the traditional training methods. The relevant methods proposed in this paper can significantly enhance the practical application value of fault prediction models based on deep learning methods in the field of intelligent maintenance of track circuit.