In order to solve the problems of insufficient dynamic information processing and edge detail capture in colorectal polyp image segmentation, such as boundary information loss and wrong segmentation, this paper proposes a colorectal polyp segmentation method based on Swin Transformer framework. Firstly, Transformer encoder is used to extract the pathological features of the image step by step. Secondly, the improved second-order channel attention (SOCA) mechanism is used to enhance cross-level information interaction ability and effectively extract rich multi-scale context feature information. Furthermore, the discrete cosine transform (DCT) in the attention mechanism of reverse frequency channel is used to highlight the channel characteristics of multi-scale context information. Finally, the image features are enhanced from both dynamic and static depth through the cross-cue fusion (CCF) module to improve the dynamic information processing and detail capture capabilities. When tested on the datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and ETIS-LaribPolypDB, Dice indices are 0.942, 0.924, 0.800 and 0.774, respectively. The MIoU indices are 0.896, 0.878, 0.726 and 0.697, respectively. The experimental data show that the proposed method can effectively segment colorectal polyp images and provide a new idea for the diagnosis of colorectal polyp.