With the development of science and technology, people began to use video image segmentation technology to carry out various research works on sports, to expect effective athlete training effects. Fuzzy clustering algorithm can accurately and quickly extract the distorted data in the process of sports video image segmentation. In order to ensure that the motion curve is not easily affected by noise in the drawing process, this paper studies the sports video segmentation strategy. The fuzzy clustering algorithm can accurately and quickly extract the distortion data in the process of segmentation of sports video images. Therefore, the motion curve is not susceptible to noise during the drawing process, which can be ensured. After completing the above-mentioned sports video segmentation strategy completion experiment, the Release to Manufacturing (RTM) model is used to evaluate the experimental results of the sports video segmentation strategy. According to the RTM model test results, the result of the homogeneity test of variance is that since the result is much larger than 0.10, this can infer that the image quality obtained by the sports video segmentation experiment has reached the Spearman Rank Order Correlation Coefficient (SROCC) standard. Experiments verify the feasibility of applying fuzzy clustering algorithm and moving video image segmentation technology to the segmentation of human model moving video image, so as to obtain more accurate image data.