In order to improve the accuracy of human body image segmentation under complex background, a new human body image segmentation algorithm is proposed. This algorithm solves the problem of specifying the number of pixel blocks in the super-pixel block segmentation for the simple linear iterative algorithm (SLIC). By referring to the CV energy model, it is constructed by minimizing the image into multiple regions for horizontal set iterative segmentation. The adaptive super-pixel block is made such that each super-pixel block after the segmentation fits a single color block in the image. Then combined with the human body average template, the human body standard posture area of interest is marked on the picture, which improves the anti-interference ability of the algorithm against the complex background. Finally, each super-pixel block is clustered as a node by k-means clustering algorithm to realize standard human body image segmentation. The experiment is carried out by collecting multiple sets of pictures in different environments. The results show that the proposed algorithm improves the segmentation accuracy of the human body's standard posture and ensures strong anti-interference ability for complex backgrounds with rich chroma.