Accurate identification and positioning of tank targets is an important research in information warfare.Aiming at the problems of insufficient timeliness and low accuracy of traditional detection algorithms, a detection algorithm based on improved YOLOv5 tank automatic identification was proposed. The YOLOv5 model is used to identify tank targets in complex battlefield environment. The Attention-based information fusion module is introduced into the basic model of YOLOv5 to improve the detection accuracy and identification ability of the model. The Pre-segment multi-scale fusion module is used to solve the problem of information loss caused by pooling operations in backbone network. Use Swin Transformer to reduce the leakage rate of small target tanks. The experimental results show that compared with the original YOLOv5 model, the accuracy rate, recall rate and average accuracy of the improved model are increased by 0. 9%, 11% and 5. 7%, respectively. The improved YOLOv5 model can accurately identify tank targets in a complex environment with a large field of vision, reducing the problem of tank small targets missing detection.