Traditional backbone convolutional networks for feature extraction in few-shot image classification suffer from the problems of single context information, limited receptive field, and lack of global edge feature similarity measurement. In this paper, we propose a few-shot image classification algorithm based on prototype embedding graph network. First, the feature obtained by multiplying the weight values generated by CBAM with the features of different scales obtained by ASPP at different sampling rates is used as the node embedding feature of the graph network. Then, the prototype nodes are constructed in the measurement module using the prototype network method, transforming the similarity calculation between paired nodes into the sum of the similarity between a single node and the prototype node, and using the obtained similarity as the edge feature input to the graph neural network. Finally, the label information is propagated from labeled samples to unlabeled samples through the dual graph structure after multiple update iterations. In the classification task using ResNet12 as the backbone convolutional network, our algorithm achieves classification accuracies of 71.47%, 75.41%, 86.21%, and 79.84% on the miniImageNet, tieredImageNet, CUB-200-2011, and CIFAR-FS datasets, respectively, for the 5way-1shot task. In the backbone convolutional network using Conv-4, our proposed algorithm outperforms existing graph network methods in both 5way-1shot and 5way-5shot tasks.