With the development of computer vision technology, deep learning-based pose estimation and target detection have been widely used in the fields of human behavior analysis and intelligent security. However, owing to the complexity of animal poses and the diversity of species, the existing pose estimation methods still face many challenges when applied to animal targets. To solve this problem, an improved YOLO-Pose model is proposed to improve the accuracy and efficiency of animal pose estimation. On the basis of the original YOLO-Pose model, a separable kernel attention mechanism is introduced and improved to make it conform to the animal target, and combined with the spatial pyramid pool of YOLO-Pose, the multiscale feature fusion capability of the model is improved. The experimental results show that the improved YOLO-Pose model achieves excellent performance on both the public animal pose dataset and the AP-10K dataset, significantly improving the ability of target detection and pose estimation.