With the advancement of computer technology,the existing Transformer has been expanded into a network structure for processing computer vision tasks.In order to improve the early diagnosis rate of melanoma and the cure rate of skin disease patients, this paper proposes an improved network model based on PiT (pyramid pooling transformer) to realize automatic classification of dermoscopic images of seven skin lesions.The model of this paper is mainly composed of the PiT module and the anti-interference module.Pit inhe rits the advantages of ViT and uses pooling to perform spatial size conversion to improve the robustness of the model.The pre-trained PiT network has a large number of natu ral image features,and the PiT part of the network can provide the required image feature s for downstream classification tasks.In this paper,an anti-interference module is designed to resist the influence of interference factors (such as hair and foreign object oc clusion) in the dermoscopic image,thereby improving the performance of the model.Improve class ification accuracy.Experimental results show that the classification accuracy,precision, recall,and F1-score values of this model on the ISIC 2018verification set are as high as 91.58%,83.59%, 89.92%,86.34%,and the number of frames per second (FPS) reaches 85Hz.Compared with several existing advanced classification networks,the classification performanc e and model efficiency have been improved,and it has relative advantages,which proves that the model in this paper has certain practical value.