Aiming at the problems of complex fault types and low diagnosis accuracy of section jointless track circuit, a fault diagnosis method of least squares support vector machine(LSSVM)optimized by deep belief network(DBN)and marine predators algorithm (MPA) is proposed from the two aspects of fault feature extraction and feature classification. Firstly, the centralized monitoring data and status labels are input into DBN, and the dimensionality reduction feature extraction is carried out in a semi supervised way, so as to mine the different fault feature information of track circuit. Then, the intelligent algorithm MPA is used to optimize the penalty factor and kernel function parameters of LSSVM, and the optimal MPA-LSSVM diagnosis model is established. Finally, the feature samples extracted by DBN are introduced into the diagnosis model for fault classification and identification of track circuit. DBN-MPA-LSSVM diagnostic model makes full use of the advantages of layer by layer extraction of DBN in the process of feature extraction and the advantages of LSSVM in solving high-dimensional pattern recognition in the case of small samples. Experimental validation and comparative analysis show that the DBN-MPA-LSSVM model test set accuracy is 98. 33%, and the MPA optimization algorithm improves the diagnosis accuracy by 6. 11%, 3. 89%, and 3. 33% compared with PSO, GWO, and GA algorithm models, respectively, with an average accuracy of 97. 98%, which provides a new data-driven rail circuit fault diagnosis technology based on method.