Objective Although the existing Transformer model has high accuracy in segmenting colorectal polyps with complex morphology the distraction of the Transformer model and the loss of information in the fusion of its encoder outputting multilevel semantic information limit the further improvement of the model?? s accuracy. Based on this a novel image segmentation model the Dual-Channel Aggregation Transformer R-DCAformer for intestinal polyps was proposed. Methods The R-DCAformer model used a pyramid mix Transformer MIT and Resnet18 to act as an encoder and a dual- channel aggregation DCA module was designed to act as a decoder. The DCA decoder consisted of an attention aggregation AA module and a dual-channel feature fusion DFF module. In this model the pyramid MIT encoder provided sufficient generalization ability for the model the AA module limited the distraction in the model MIT by fusing the additional features of Resnet18 and the DFF module alleviated the problem of information loss in the fusion of multi-level semantic information. Results In the generalization ability experiment R-DCAformer improved the optimal mDice mIoU and MAE by 2. 10% 1. 65% and 22. 5% respectively in CVC-ColonDB compared with the optimal ones in the baseline model. The optimal mDice mIoU and MAE in ETIS were improved by 2. 56% 2. 12% and 15% respectively compared with the optimal ones in the baseline model. The model improved the optimal mDice and mIoU by about 0. 85% and 1. 35% in the CVC-ClinicDB dataset compared with the optimal ones in the baseline model and the optimal mDice mIoU and MAE on the Kvasir-SEG dataset were improved by about 1. 19% 1. 97% and 17. 39% respectively compared with those in the baseline model. The effectiveness of the module proposed in this paper was also demonstrated by ablation experiments and attention graphs. Conclusion R-DCAformer is more effective in both learning and generalization experiments and generally outperforms the compared baseline models providing a new high- performance model for colorectal polyp segmentation.