Learning with limited data is a challenging field for computer visual recognition. Prototypes calculated by the metric learning method are inaccurate when samples are limited. In addition, the generalization ability of the model is poor. To improve the performance of few-shot image classification, the following measures are adopted. Firstly, to tackle the problem of limited samples, the masked autoencoder is used to enhance data. Secondly, prototypes are calculated by task-specific features, which are obtained by the multi-scale attention mechanism. The attention mechanism makes prototypes more accurate. Thirdly, the domain adaptation module is added with a margin loss function. The margin loss pushes different prototypes away from each other in the feature space. Sufficient margin space improves the generalization performance of the method. The experimental results show the proposed method achieves better performance on few-shot classification.