Objective To explore the classification of 4 common pulmonary ultrasound signs based on deep residual network ResNet152.Methods We prospectively collected ultrasound images of A lines,B lines,pleural effusion and lung consolidation (1 500 at each site) from Jun.to Sep.2020 in Shanghai Pulmonary Hospital of Tongji University.Images that were clear,uncomplicated and not obscured by bones were selected.Finally,1 388 images of A lines,1 375 images of B lines,1 384 images of pleural effusion,and 1 398 images of lung consolidation were included.Deep residual network ResNet152 was used to train and verify the classification model,and the generalization ability of the model was tested on the test set completely independent of the training set and the validation set.Accuracy rate,precision rate,specificity rate,recall rate and F1-score were used as the evaluation criteria for classification,and the classification results were visualized by the confusion matrix.Results The accuracy rates of the classification model based on the deep residual network to classify the 4 signs of A lines,B lines,pleural effusion and lung consolidation were 97.51%,87.31%,85.42% and 93.70%,respectively,and the recall rates were 90.38%,86.97%,94.25% and 91.18%,respectively.The overall classification accuracy rate of the 4 lung signs was 90.99%,the precision rate was 90.70%,the specificity rate was 96.85%,and the F1-score was 90.50%,showing excellent classification characteristics.Conclusion The pulmonary ultrasound classification model based on deep residual network shows high classification characteristics,and has potential to assist ultrasound diagnosis.