The abnormal sound in the trains running can be used as information source to reflect the status of the vehicle equipment. For the reason that, a recognition model based on 1D-CNN was proposed to identify the abnormal sound of trains, and a set of recognition system for abnormal sound of trains was designed. Firstly, the experimental sample library of audio data was constructed. Then MFCC was used to extract the characteristic information of abnormal sound data samples. Aiming at the complex mapping relationship between train noise features and vehicle state types, a 1D-MCNN based on MFCC input was constructed to identify and classify the fault information contained in abnormal sound. Finally, the model parameters such as MFCC order, learning rate and batch size are determined by experiments and optimization. The t-SNE algorithm and confusion matrix were used to analyze the model feature extraction ability. The results show that the method is effective for the identification and diagnosis of abnormal sound of trains and its accuracy rate reaches 98.38%.