In order to solve the problem of pattern recognition of gas pressure detection in natural gas pipeline, the original signal is preprocessed to remove redundant information, and then the signal is decomposed by variational mode decomposition to extract the optimal Intrinsic mode function and reconstruct the signal. Then, the processed signal is transformed into a high-resolution twodimensional image in time and frequency domain by continuous wavelet transform. Finally, the image is extracted by deep convolution neural network, and the output of part of the network is connected with support vector machine to realize supervised learning and training. The trained support vector machine is used for unsupervised pattern recognition of the remaining data. Experiments show that the accuracy of vmd-cnn-svm is 90. 66%, which is the highest compared with other methods.