In the process of oil and gas pipeline leak detection, the leak signal contains a lot of noise and the feature extraction is difficult. An improved total average empirical mode decomposition combined with Kalman filter algorithm is proposed to denoise the pipeline signal. First, the improved overall average empirical mode algorithm is used to decompose the collected pipeline negative pressure wave signal. The permutation entropy and Kalman filter algorithm are used to filter and process the decomposed inherent modal components, and finally the reconstructed Cut the noise signal. Furthermore, a feature extraction method based on diffusion entropy and kurtosis is proposed, the extracted feature parameters are used as the input of support vector machine to classify and recognize the working conditions of oil pipelines. The collected data verify that the improved overall average empirical mode decomposition, Kalman filter, spread entropy and kurtosis combined recognition method can more accurately classify and recognize pipeline signals, and the results show that the total average recognition accuracy is 98.89. %, it provides a new way for the research of pipeline working condition identification.