The goal of few-shot image classification is to identify the category based on a very small number of labeled samples. Two of the key issues are too little labeled data and invisible categories (the training category and the test category are inconsistent). In response, we propose a new few-shot classification model: fusion expansion-dual feature extraction model. First, we introduce a fusion expansion mechanism (FE), which uses the change rules between different samples of the same category in the visible category samples to expand the support set samples, thereby increasing the number of samples in the support set and making the extracted features more robust. Secondly, we propose a dual feature extraction mechanism (DF). A large amount of data from the base class is first utilized to train two different feature extractors: a local feature extractor and a global feature extractor, which are applied to extract more comprehensive sample features. Then the local and overall features are compared to highlight the features that have the greatest impact on the classification, thereby improving the accuracy of the classification. On the Mini-ImageNet and Tiered-ImageNet datasets, our model has achieved good results.