Aiming at the problem that traditional Image registration methods have poor effect in infrared and visible Image registration tasks. A composite 2S network, Superpoint+Superglue, is proposed for infrared and visible Image registration. The method first uses Superpoint's unique feature extraction method to fully extract common features between infrared and visible light images. Secondly, the idea of adding matching constraints and using attention mechanism in Superlube feature matching method is used to give full play to the advantages of neural network and improve the matching efficiency. In the training phase, the method of using self built datasets is used to improve the generalization and accuracy of the neural network. The results show that the repeatability and accuracy scores of traditional registration methods for feature point extraction on three sets of experimental images are (0.0067, 0.0061), (0.0010, 0.0008), and (0, 0), respectively. The correct matching logarithms of feature points are: 7 pairs, 1 pair, and 0 pairs, with an average number lower than the minimum four matching point pairs required to estimate the transformation matrix. The scores of infrared and visible Image registration methods based on Superpoint+Superglue are (0.2402, 0.2625), (0.1939, 0.1722), (0.2630, 0.2644), and the correct matching logarithms of feature points are 252, 165, and 252 pairs. The evaluation index of feature point extraction and the number of correct matching of feature point pairs are significantly increased compared with traditional methods, which can better complete the registration task.