Topological data analysis (TDA) method could catch the rich geometric and topologic information of big data and find subtle differences between different signals. TDA method opens up a new way for biomedical data analysis. In this study, we applied TDA method for heart sound signals (PCG) classification. First, the sliding window method was used to build a point cloud. Then, the persistent barcode is extracted from the point cloud by using the topology technology Vietoris-Rips (VR) filtration. At last, GoogLeNet transfer learning model was applied for classifing. The proposed the model did work well on the 2016 PhysioNet/CinC challenge dataset, Se=99.30%, +P=99.57%, F1=99.44%, mAcc=99.47%. The results showed that TDA can be used for the analysis of physiological signals in large quantities. The proposed method in this study has opened a new space for the application of TDA methods in physiological signal analysis.