In response to the problem of low segmentation accuracy in colon polyp images due to varying sizes， complex shapes， and unclear boundaries between polyps and mucosa， a progressive CNN-Transformer semantic compensation polyp segmentation network was proposed to improve the segmentation accuracy of colon polyps. In order to better utilize the local features from the CNN encoder and the global features from the Transformer encoder， a same-layer feature interaction coupling module was designed to adaptively fuse features from the CNN and Transformer encoders in both spatial and channel dimensions through grouped interaction coupling. Then， to address the issue of semantic loss caused by upsampling during the decoding process， a Query-based semantic compensation module was designed. This module gradually integrates and distributes image semantics through a set of learnable descriptors， effectively enhancing the network's feature discrimination capability. The experimental results show that the proposed network achieved mDice scores of 94.23%， 90.36%， 92.93%， and 80.26% on the CVC-ClinicDB， CVC-300， Kvasir， and CVC-ColonDB public datasets， respectively. The mIoU scores are 89.87%， 83.75%， 88.21%， and 72.09%， respectively. Compared to existing segmentation networks， the proposed network can effectively improve the effectiveness of polyp segmentation while ensuring its generalization.