In order to further improve the accuracy of breast tumor segmentation, a breast tumor segmentation model based on simple linear iterative clustering (SLIC) and grandient vector flow (GVF) Snake algorithm was proposed. The model first preprocesses the image to reduce redundant information and improve subsequent segmentation efficiency. Secondly, an adaptive value method is proposed based on the texture features of the image, and the image is roughly segmented by SLIC algorithm to describe the initial contour of the breast mass. Finally, the GVF Snake algorithm is used to increase the capture range of the contour edge information, and the segmentation result is obtained by fine segmentation. Experimental results show that the segmentation model can effectively improve the segmentation efficiency and accuracy, which is better than the traditional segmentation algorithm to some extent, and the ideal segmentation results are obtained.