For rolling bearing fault diagnosis,inspired by traditional manual auscultation practices,to solve the problem that the fault features extraction by traditional machine learning method depends too much on manual work,and the diagnosis accuracy is low,a new rolling bearing fault diagnosis method based on acoustic signal and one-dimensional convolutional neural network model was proposed.The sound signals of the bearing in different health states were obtained by using an electronic stethoscope.The rotation period of the bearing was taken as the data sample length,and the resampling data set enhancement method was used to improve the generalization of the model.A one-dimensional convolutional neural network deep learning model based on TensorFlow was built for experimental verification,and t-SNE was used to visualize the classification process.The diagnostic accuracy reaches 99%.