To resolve the problem that the available data on the ground-to-air infrared aircraft identification task is considerably scarce, the small samples infrared aircraft identification classification method is proposed on the basis of an improved relation network. This method combines the relation network model and the multi-scale feature fused method with the meta learning training strategy. First, a multi-scale feature extraction module is constructed to extract the feature tensors of input images. Then, the feature tensors of support samples and test samples are inputted into the relation module, and the category labels corresponding to test samples are predicted based on the relation value. The results of the proposed model on the mini-ImageNet dataset show that the classification accuracy of the proposed model is significantly higher than those of other conventional learning models using small samples. The experimental results based on the Infra-aircraft dataset verify that the proposed model can realize the ground-to-air infrared image classification task of various aircraft types even when the number of samples is limited.