Abstract: Pressure pipelines are widely used in the petrochemical industry, and when using medium and low frequency ultrasonic guided waves to detect and locate defects such as corrosion and thinning of pressure pipelines, the detection signal is attenuated and the signal-to-noise ratio is reduced due to factors such as poor surface condition of the pipeline, thick anti-corrosion layer and burial, and it is easy to miss the detection of small defects. In order to solve the problem that it is difficult to effectively process such signals by common time-frequency methods, Multi-scale Recursive Quantitative Analysis (MRQA) based on ensemble empirical mode decomposition was used to improve the ability of pipeline defect identification. Firstly, according to the non-stationary characteristics of the guided wave signal of the noisy artificial defect pipeline, the multi-scale signal is obtained by ensemble empirical mode decomposition, and eight recursive quantitative parameters are obtained by recursive quantitative analysis of the multi-scale signal, and the appropriate recursive quantitative curve is compared and determined to complete the axial accurate positioning of the small defects. The experimental results show that the signal processing algorithm has a good identification and positioning effect on the small corrosion defects of pipelines in the noisy environment, and the capture time (TT) and the second type of recursive time (T2) are determined to be the main reference indexes, and the axial positioning error is less than 10%, and the stability and effectiveness of the algorithm are verified in the field detection.