The widespread application and militarization of UAVs have brought serious harm to the national and so- cial security. Aiming at the problems of legislative bottleneck in the civil deployment of traditional anti-UAVs system equipment and the lack of simultaneous detection and recognition mechanism of multiple UAVs ,a UAVs target recogni-tion technology based on YOLOv7 was proposed. Using YOLOv7 network to identify UAV targets in high-altitude multi-scene environment : a Feature reuse based on concatenation module is introduced in YOLOv7 to solve the problems of limited feature reuse in backbone and information loss in deep network. ELAN of attention mechanism module is used to improve the ability of removing noise and suppressing irrelevant information in feature fusion. The HEAD of expan- sive convolution and residual theory is used to reduce the problem of missing detection of small target UAVs. The re- sults show that compared with the original YOLOv7 model ,the average accuracy of the improved model is increased by 2. 8% ,which solves the problem of missing detection of small targets in the original network and makes up for the shortcomings of anti-UAV in civil application.