In this work, a novel convolutional neural architecture (SE-CNN), which combines spectrum enhancement (SE) and convolutional neural network (CNN), is proposed to detect the leak of gas pipeline. The SE has the effect of enhancing the leak signals and reducing background noise. CNN can automatically extract leak features and realize leak diagnosis. The experimental results show that the SE-CNN can achieve an average accuracy of 94.3% for 6 categories and only requires 1.04 s of detection time. In this experiment, the diameters of the main pipeline and the branch pipeline are 125 mm and 25 mm. Due to its excellent accuracy and efficiency, the proposed enhanced spectrum convolutional neural architecture paves the way for real-time leak detection in industrial environments, which can ensure the process safety of gas pipeline transportation. Under strong background noise, the average accuracy of the SE-CNN can reach 94.3%, which is 33%, 3.7% higher than that of SVM and CNN. In particular, the SE can be regarded as a data compression method, which can significantly reduce the original data size. The training time of the SE-CNN is 539 s, reducing 90.6% compared with CNN. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.