In order to solve the problems of the insufficient feature information extraction and the difficulty in capturing local obvious feature information accurately in few-shot learning， a method combining class enhancement and multi-scale adaptation is proposed. Firstly， the class enhancement is performed on the image at the level of features， and rich semantic structures are encoded by associating each activation of the feature map with its neighborhood， thus making the extracted intra class features obvious and more conducive to the current classification task. Secondly， low-level representations of image features at different scales are extracted through multi-scale feature generation. Finally， the semantic correlation matrix on each scale is weighted and similarity elements are maximized to calculate the semantic similarity between the query image and each support set category image. After the fusion of multi-scale information， the target images are classified. In the 5-way 1-shot and 5-way 5-shot settings， the mean average precision （mAP） of this method on the miniImageNet dataset is 56.83% and 75.76% respectively， and it achieves 79.33% and 93.92%， 66.33% and 85.78% on the commonly used fine grained image dataset Standard Cars and CUB-200-2011 classification benchmarks， respectively， which are superior to the best results of the existing methods.