Objective To examine the heart sound signals of four common congenital heart diseases of ventricular septal defect, atrial septal defect, patent ductus arteriosus and patent foramen with associated pulmonary hypertension and propose a deep learning-based intelligent auscultation algorithm for automatic classification of heart sound signals. Methods The algorithm in this study was based upon digital signal processing technology of converting one-dimensional temporal signal classification into a two-dimensional image classification and further applying deep neural networks for automatic classification of heart sound signals. A total of 941 heart sound data samples collected from Children's Hospital of Zhejiang University School of Medicine were employed for training, validation and testing with a ratio of 8 1 1 for training, validation and testing sets respectively. Additionally, 107 heart sound data samples gathered from a clinical screening environment were collected to validate the effectiveness of intelligent auscultation algorithm in real-world clinical applications. Results In this study, discrete wavelet transformation was utilized to denoise the heart sound signals and there was a significant improvement in model performance. Compared to model without denoising, the denoised model achieved no- table enhancements in accuracy, sensitivity, specificity and F1 score on testing set with improvements of 15. 8%,32.6%,11.1% and 27.3% respectively. Furthermore, the authors compared the performance of several common classification neural network models, including Swin_transform, Vit, Mobilenet, Resenet and Vgg, with their respective F1 scores of 0.905,0.842,0.687,0.814 and 0.864. Finally, using Swin_transform model, tests on the external dataset of 107 cases yielded an accuracy of 0.833, a sensitivity of 0.872 and a specificity of 0. 801. Conclusion This study highlights the significant impact of noise and neural network structure on the performance of automatic classification models for CHD heart sound signals. Through the application of discrete wavelet transform for denoising heart sound signals, a substantial improvement in model performance is observed. Among various common classification neural network models, Swin_transform model exhibits the best classification performance. Additionally, the validation of intelligent auscultation algorithm on an external dataset of 107 cases demonstrates its effectiveness in real-world clinical applications, yielding favorable accuracy, sensitivity and specificity results. In summary, this study demonstrates the promising potential of deep learning-based intelligent auscultation algorithms for automatic classification of CHD heart sound signals. [ABSTRACT FROM AUTHOR]