This study is dedicated to enhancing the application efficiency and accuracy of deep learning in lung sound analysis. In view of the insufficient robustness and limited generalization capabilities of existing deep learning models in lung sound analysis, it proposes a method that integrates the convolutional neural networks (CNN), long short-term memory network (LSTM), and support vector machine (SVM) to achieve efficient and in-depth analysis of lung sound signals. The method begins with the preprocessing of lung sound signals to extract reconstructed signals and their corresponding Hilbert spectra. Secondly, a deep learning network model that integrates CNN, LSTM, and SVM is designed and built. Finally, the processed signal data are input into the CNN-LSTM-SVM deep learning network to extract and fuse the time-domain and frequency-domain features of lung sound signals. Experimental results show that the method achieves high levels of 96.20% for the recall, 96.56% for accuracy, and 0.96 for F1-score. These results confirm the efficiency and reliability of the proposed method, providing a new technological approach for the early diagnosis of lung diseases, and potentially significantly enhancing the speed and accuracy of clinical diagnosis.