Abstract In order to classify interstitial diseases more accurately, this paper proposes a classification network based on deep learning Xi, which first combines the multi-head self-attention mechanism module with DenseNet-121, so that the model can focus on multiple key regions at the same time. Then, the convolutional attention module is used to achieve more efficient feature extraction and spatial perception capabilities, so as to improve the classification ability of the network. Finally, an improved spatial pyramid pooling layer is added to stitch together the feature maps of different scales to capture richer spatial information. In addition, aiming at the problem of category imbalance of high-resolution CT image datasets, the Focal Loss function is introduced, which makes the model focus more on the difficult samples during training, so as to further improve the classification ability of the model. The proposed method is tested on the untrained dataset in this paper, and the accuracy rate reaches 88.28%. Compared with the original DenseNet-121, the accuracy, recall, precision, F1 score and Kappa coefficient are increased by 4.65%, 5.08%, 5.82%, 5.45% and 6.38% Experimental results show that the proposed method has the characteristics of strong feature extraction ability and high classification accuracy.