Aiming at internal leakage failures of gas transmission pipeline valves in petrochemical industry, this paper proposes an identification method of acoustic emission signal of valve internal leakage based on full convolutional neural network ( FCN) by combining acoustic emission detection technology with deep learning technology. The method uses acoustic emission technology to collect acoustic emission signal of valve internal leakage and builds a valve internal leakage classification and diagnosis model based on FCN, which fully exploits the superiority of acoustic emission technology in the field of valve internal leakage detection and the high performance of FCN in time series classification tasks. Compared with traditional identification methods, this method does not require any feature extraction or complex preprocessing of the original collected data. Instead, the task of feature extraction is also handed over to the neural network model to learn and complete, which can realize end-to-end classification and identification of valve internal leakage acoustic emission signal. The data sets of valve internal leakage acoustic emission signal are collected and produced through the experimental platform of valve internal leakage detection, and the binary classification model of valve internal leakage acoustic emission signal based on FCN is established as well. The experimental results show that the accuracy of classification and recognition of the model can reach 98. 72%. Compared with other advanced classification models, the model shows more superior recognition performance on the data sets and has higher training efficiency, which also has good anti-interference performance against environmental noise at the same time.