In the ultrasonic detection of corrosion defects of natural gas steel pipelines, the conventional pattern recognition method adopts the method of manually extracting echo signals, which has the problems of strong subjectivity and low universality. Based on this, this paper proposes a method to extract the features of echo signals by using one-dimensional convolutional neural network and classify the features by combining with random forest. Firstly, according to the noise of the echo signal, the wavelet packet transform is used to denoise the signal. The denoised signal is decomposed and reconstructed by variational modal decomposition to obtain a smooth signal. Finally, the processed echo signals are extracted by 1D-CNN network features and classified by random forest. The experimental results show that the identification accuracy of the method based on VMD-1D-CNN-RF is 85. 71% for artificial defects and 71. 05% for pipeline defects in natural gas stations, indicating that the pipeline condition can be preliminarily identified without expert identification.