Abstract: In recent years, acoustic emission technology has been widely applied in pipeline operation safety monitoring and structural integrity maintenance. However, the diversity of weld crack form in pipelines leads to the complexity of time-varying acoustic emission signal characteristics, which limits the improvement of monitoring accuracy. Therefore, this paper proposes a pressure pipeline leakage monitoring system based on Markov transfer field image processing technology(MTF-IPT) and Residual Swin Transformer model. The method first encodes one-dimensional acoustic emission signals into two-dimensional images using MTF, which enhances the correlation and temporal dependence between time-series sampling points while revealing the multi-dimensional phase space trajectories of the signals. Subsequently, a Residual Swin Transformer deep learning network is constructed to extract key feature information of leakage states from the two-dimensional images, enabling the identification of different leakage states. Finally, experiments with various leakage states are designed to verify the superiority of the proposed method in multiple evaluation indicators, achieving an ideal recognition accuracy of 97.86%. Comparative experiments with other methods further demonstrate the superior performance of the proposed method for online pipeline leakage monitoring, and it can be deployed for pipeline operational safety maintenance.