Accurate segmentation of colon polyps is important to remove abnormal tissue and reduce the risk of polyps converting to colon cancer. The current colon polyp segmentation model has the problems of high misjudgment rate and low segmentation accuracy in the segmentation of polyp images. To achieve accurate segmentation of polyp images, this study proposes a colon polyp segmentation model (MGW-Net) combining multi-scale gated convolution and window attention. Firstly, it designs an improved multi-scale gate convolution module (MGCM) to replace the U-Net convolutional block to achieve full extraction of colon polyp image information. Secondly, to reduce the information loss at the skip connection and make full use of the information at the bottom of the network, the study builds a multi-information fusion enhancement module (MFEM) by combining improved dilated convolution and hybrid enhanced residual window attention to optimize the feature fusion at the skip connection. Experimental results on CVC-ClinicDB and Kvasir-SEG data sets show that the similarity coefficients of MGW-Net are 93.8% and 92.7%, and the average crossover ratio is 89.4% and 87.9%, respectively. Experimental results on CVC-ColonDB, CVC-300, and ETIS datasets show that MGW-Net has strong generalization performance, which verifies that MGW-Net can effectively improve the accuracy and robustness of colon polyp segmentation.