In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit, a few-shot infrared aircraft classification method based on cross-correlation networks is proposed. This method combines two core modules: a simple parameter-free self-attention and cross-attention. By analyzing the self-correlation and cross-correlation between support images and query images, it achieves effective classification of infrared aircraft under few-shot conditions. The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner. The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images. Compared with existing few-shot infrared target classification models, this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set, thus better attending to the target objects. Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks, with the highest classification accuracy improvement exceeding 3%. In addition, ablation experiments and comparative experiments also prove the effectiveness of the method.