In order to improve the accuracy of pipe percussion detection in the wall, this paper uses the fine composite multi-scale dispersion entropy to detect the frequency and amplitude changes of the percussion sound signal, and extract the multi-scale pipe features in the signal. The multi-dimensional pipeline feature matrix was input into the support vector machine, and the sparrow search algorithm was used to determine the optimal value of the parameters of the support vector machine. The classification of buried pipelines in the wall was completed through model training, and a knocking detection method of pipelines in the wall based on fine composite multi-scale dispersion entropy was proposed. Comparing this method with other signal processing methods, the results show that the detection accuracy of the proposed method is up to 97%, which is much higher than the other two methods.