Abstract: The engineering application of automatic target recognition is the key technology to realize the long-range and precise strike after the image terminal-guided missile is launched. The development history, identification method, technical level and application effect of automatic target recognition of precision-guided weapons at home and abroad are summarized. The recognition methods and application scenes based on target features and template matching are analysed, and two types of engineering verification effective methods are identified. The automatic target recognition method combines the automatic target recognition process, such as task planning, main execution content, and the impact of planning quality on different recognition methods. To meet the needs of intelligent development of precision guided weapons in the future, the engineering application of deep learning recognition technology has become a new trend. To solve the balance problem between the efficiency and application accuracy of deep learning algorithms, this paper focuses on the analysis of network pruning, weight quantization, and low rank. The key technologies of real-time acceleration inference such as approximation and knowledge distillation; for network model training, ideas for effectively solving problems such as insufficient training samples or difficulty in obtaining military target samples are proposed. With the wide application of multiband and multimode composite guidance technology, information fusion provides a new technical approach for the engineering application of target recognition. How to adapt to various complex scenes and artificial active interference is a major challenge for image terminal guidance. The robustness of target recognition under interference conditions is expounded, which is an engineering problem that needs to be urgently solved in the application of automatic target recognition technology in image terminal guidance.