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.