Abstract: Given that the common ultrasonic detection method fail to carry out long distance detection of rail transit tracks and the SHM (structural health monitoring) technology based on ultrasonic guided waves has difficulty extracting damage features from response signals, affecting the accuracy of damage locating. A locating method using piezoelectric array ultrasonic guided wave based on Wav2vec2.0 neural network is proposed for rail transit track damages. With reference to the characteristics of piezoelectric array ultrasonic guided wave data, this method is briefly introduced. An ultrasonic guided wave detection system for track damages is established and utilized for data collection. A 3D finite element model for ultrasonic guided wave detection of track damages is built using ABAQUS finite element software, and used to collect the dataset. Wavelet signal processing is applied to reconstruct the ultrasonic guided wave test signals for signal denoising; random noise is superposed to the simulation signals and the ultrasonic guided wave simulation signals with the superposed random noise are adopted as the supplementary dataset; the performance of the model is evaluated by calculating the accuracy rate and error of rail damage locating. The results show that when the iteration reaches the 120th round, the accuracy of the training samples reaches 100%. By adopting the piezoelectric array ultrasonic guided wave locating method based on Wav2vec2.0 neural network, the accurate locating of rail transit track damages can be achieved.