In view of the small and unobvious target of the UAV and the re-tracking problem after the target is occluded, an anti-UAV recognition and tracking algorithm, YOLO-G-KCF, which integrates improved YOLO and improved KCF models, is proposed. This algorithm introduces multi-channel gradient features and original image features into the YOLOv10 algorithm by means of feature concatenation in feature processing. Therefore, the improved algorithm has a better detection effect on targets under strong light, shadow, and other complex lighting conditions. Meanwhile, the multi-channel gradient features are introduced into the KCF target tracking algorithm, and a multi-scale detection is designed to make the KCF algorithm have good scale adaptability. The KCF tracking results are introduced after the Head, and the new loss function Inner-IoU is calculated to more accurately identify the tracking target. The experimental results show that the YOLO-G-KCF algorithm achieves a 95.3% accuracy rate when tested on the dataset comprising multiple open-source UAV video target tracking. This is in comparison with the original model of YOLOv10, wherein the improved model′s mAP@0.5 has an increase of 1.37%, and the average precision mAP@0.5 reaches 94.28% and the recognition speed reaches 112 FPS, which can operate at more than 100 FPS to satisfy the real-time requirements of UAV target recognition and tracking. Compared with other algorithms without sacrificing speed, introducing recognition mechanisms for tracking and improving them has better recognition and tracking effects. YOLO-G-KCF algorithm realizes the recognition and tracking of low-speed, small-sized, and low-altitude unmanned aerial vehicles in situations where the target is small, not prominent, and occluded. It has high recognition accuracy, strong antiinterference ability, good real-time hardware development, and certain theoretical research and engineering application value.