Accurate identification of multi-rotor UAVs in different environments is crucial in no-fly zones. However, due to the small size and fast flight speed of multi-rotor UAVs, it is difficult to identify them quickly and accurately. This paper proposes a small target detection algorithm, YOLOv7-SPD3, based on YOLOv7 to realize the accurate recognition of multi-rotor UAVs in long-distance states. Firstly, the complexity of the original UAV data set is improved by data enhancement. Secondly, the recognition accuracy of small long-distance targets is enhanced by adding SPD-Conv to the YOLOv7 backbone network. Thirdly, the small target detection ability is further improved by replacing the original loss function with Wise-IOU. Fourthly, the proposed model is fine-tuned to achieve accurate recognition of UAVs. The experimental results obtained by using the workstation in the Python3.8 environment show that the YOLOv7-SPD3 algorithm is superior to YOLOv7, YOLOv7-Tiny, SSD300, and YOLOv3. The maximum precision value is increased by 2.2%; the maximum Recall value is increased by 60.42%, the maximum mAP@0.5 value is increased by 17.83%, and the maximum mAP@0.5:0.95 is increased by 30.1%, which can meet the accurate identification of small long-range targets.