To address the problem of large-scale variation， blurred boundaries， irregular shapes， and low contrast with normal tissues in colon polyp images， which leads to the loss of edge detail information and mis-segmentation of lesion areas， we propose a cross-dimensional and cross-scale adaptive transformer segmentation network. First， the network uses transformer encoders to model the global contextual information of the input image and analyze the colon polyp lesion areas at multiple scales. Second， the channel attention and spatial attention bridges are used to reduce channel dimension redundancy and enhance the model's spatial perception ability while suppressing background noise. Third， the multi-scale dense parallel decoding module is used to bridge the semantic gaps between cross-scale feature information at different layers， effectively aggregating multi-scale contextual features. Fourth， a multi-scale prediction module is designed for edge details， guiding the network to correct boundary errors in a learnable manner. The experimental results conducted on the CVC-ClinicDB， Kvasir-SEG， CVC-ColonDB， and ETIS datasets showed that the Dice similarity coefficients are 0.942， 0.932， 0.811， and 0.805， and the average intersection-over-union ratios are 0.896， 0.883， 0.731， and 0.729， respectively. The segmentation performance of our proposed method was better than that of existing methods. The simulation experiment showed that our method can effectively improve the mis-segmentation of colon polyp lesion areas and achieve high segmentation accuracy， providing a new approach for colon polyp diagnosis.