Human parsing aims to segment a human image into multiple parts with fine-grained semantics and provides more detailed understanding of image contents. When the human body posture is complicated, the existing human parsing methods are easy to cause misjudgment to the human limb components, and the segmentation of the small target is not accurate enough. In order to solve the above problems, a double-branch networkjointingposture prior is proposed for accurate human parsing. The model first uses the backbone network to acquire the characteristics of the human body image, and then uses the pose prior information predicted by the human pose estimation model as the attention information to form a multi-scale feature expression driven by the human body structure prior. The multi-scale features are fed into the fully convolution network parsing branch and detection parsing branch separately. The fully convolutional network obtains global segmentation results, and the detection parsing branch pays more attention to the detection and segmentation of small-scale targets. The segmentation results of the two branches are fused to obtain the final parsing result, which can be more accurate. The experiment results verify the effectiveness of the proposed algorithm. Our Thisapproach has achieved 52.19% mIoU on LIP dataset, 68.29% mIoU on ATR dataset, which improves the human parsing accuracy effectively and achieves more accurate segmentation results in the human limb components and small target componentsn parsing accuracy effectively and achieves more accurate segmentation results in the human limb components and small target components.