Switch machine is an important equipment to realize turnout conversion on the railway. Its operation and maintenance takes a long time, its fault identification accuracy is not high, and there are many problems such as misjudgment, omission and soon. To solve the above problems, this paper proposes a new fault recognition method for S700K switch machine based on artificial intelligence, deep learning and other new technologies. Compared with the traditional Harr or Mexicanhat wavelet decomposition, in this paper, the power curve data sampled by the microcomputer monitoring system is decomposed and composed by an orthogonal wavelet Daubechies wave with tight support, and the feature vectors of eight common types of faults are extracted, which are normalized as the input of the improved wavelet neural network. Then, the IPSOWNN fault recognition model is constructed by using the classification learning particle swarm optimization algorithm to optimize the weights and thresholds in the network. Finally, the action power curve in the station monitor data base is selected for network training and testing of the fault identification model. The algorithm proposes in this paper has a fault identification accuracy of more than 95% and takes only about 21 seconds on the 8 common fault of switch machine. It can be effectively applied to the fault identification of S700K type switch machine and improve its accuracy and speed, providing theoretical support for the prediction of fault identification of switch machine.