Abstract: Infrared sensing technology effectively handle the problem of night observation, which is becoming an important measure for battlefield reconnaissance. Continuously improving the ability of target recognition based on infrared images was a powerful way to implement precision strikes and situational awareness. Aiming at the problem of infrared image recognition, a Zernike feature selection algorithm based on Light Gradient Boosting Machine (LGBM) was proposed, combined with Sparse Representation-based Classification (SRC) to complete the target category confirmation. Firstly, based on the target area in the infrared image, multi-order Zernike moment features were extracted to characterize the essential characteristics of the target to be recognized; Secondly, the LGBM feature selection algorithm was used to screen the multi-order moment features twice to reduce redundancy and improve the pertinence of features; Finally, the final selected Zernike moment feature vector was classified based on SRC. The method effectively improves the effectiveness of the final features through the feature selection of LGBM, at the same time reduces the computational complexity of classification, which was beneficial to improve the overall recognition performance. The publicly available mid-wave infrared target image data set was used to carry out verification experiments to distinguish and identify 10 types of typical military targets. The experiment was carried out under the three conditions of original samples, noise interference samples and partially missing samples, and compared with several types of existing infrared target recognition methods. The results show that the proposed method can achieve better performance and prove its effectiveness.