Aiming at the current difficulty in nondestructive testing of pipeline gas pressure, combined with the principle of ultrasonic reflection pressure measurement, a deep belief network (DBN) extraction of ultrasonic echo amplitude characteristics is proposed, which is least square support vector machine ( LSSVM) pipeline gas pressure detection method. First, the features are extracted through unsupervised layer-by-layer learning of the restricted Boltzmann machine (RBM) in the DBN network. Secondly, the supervised error back propagation adjustment is performed through the label layer to optimize the RBM parameters of each layer of the DBN. Finally, input the characteristic signal extracted by the optimized DBN network into the trained LSSVM to complete the gas pressure recognition. Design related experiments to obtain ultrasonic data for model testing. The results show that the average relative error of pressure recognition of the DBN-LSSVM pressure recognition model proposed in this paper is 0. 635 7%, which is lower than the average relative error of the DBN-BP model (1. 802 6%), which is better complete the pressure detection of the pipeline gas.