Deep learning-based image classification methods typically require a lot of labeled data. However， in classification task of skin lesions in the medical field， collecting a lot of image data faces numerous challenges. To classify few-shot skin diseases accurately， a few-shot classification model based on Spatial Transformer Network （STN） and feature distribution calibration was proposed. Firstly， transfer learning and meta-learning were integrated to address the overfitting issue in cross-domain few-shot transfer. Secondly， a rotation angle prediction task was inserted before the pre-training classification task to better adapt the model to the high complexity of medical image data. Thirdly， after downsampling the images， a STN was introduced to perform affine transformations on the input images explicitly， thereby enhancing feature extraction and recognition capabilities. Finally， feature distribution calibration was used to constrain new class features， and the nearest centroid algorithm was introduced for classification decisions， thereby reducing algorithm complexity while improving classification accuracy significantly. Experimental results on ISIC2018 skin lesion dataset show that compared to the current mainstream few-shot model Meta-Baseline， the proposed model has the accuracy improvements of 11.80 and 10.82 percentage points in 2-way and 3-way classification tasks， respectively； compared to the model MetaMed， the proposed model has the average accuracy improvements of 6.65 and 9.58 percentage points in 2-way 3-shot and 3-way 3-shot classification tasks， respectively. It can be seen that the proposed model improves the classification accuracy of few-shot skin diseases effectively， and can assist doctors better in enhancing clinical diagnosis accuracy.