In order to quickly identify the leakage fault of heating pipeline, a method of pipeline leakage diagnosis using convolutional neural network (CNN) to identify the pressure data was proposed for the negative pressure wave characteristics of pipeline leakage.By setting up the experimental platform of heating pipeline, the pressure data under normal, leakage and regulating valve conditions are collected as the training set and test set of convolutional neural network.The original data are denoised by wavelet, and the hard threshold processing method is used to effectively eliminate the noise signal. Meanwhile, the enhancement feature appears in the valve condition, which is helpful to enhance the classification ability of the convolutional neural network.The improved AlexNet convolution network model is used to learn and identify the collected data.The results show that the average recognition accuracy of CNN model is 98.3% in laboratory data testing. In the verification of the actual pipe network, the leakage data of the three thermal stations were correctly identified, indicating that the CNN model has good fault diagnosis ability.