A polyp segmentation method based on dual path feature multi-scale subtraction is proposed to address the issues of significant size differences, unclear boundaries, and scattered distribution of colon polyps. The main branch merges adjacent feature maps by reconstruction subtraction units and attention models, enhancing the boundary information of polyps and the ability to extract polyp features. Simultaneously, a learnable visual center (LVC) is introduced to aggregate local corner key regions of the input image. In the sub-branch, a multi-scale extraction module and an inverted residual up-sampling module are designed to fuse into an AGG module (AGG) for multi-scale size polyp extraction, restoring and supplementing more detailed information. The proposed method is experimentally analyzed on four public datasets, and the experimental results show that our method has good generalization performance on polyp segmentation, with mDice and mIoU achieving 93.28% and 88.98% on the CVC-ClinicDB dataset, respectively.