With the rapid development of computer-aided medical diagnosis systems and medical image segmentation technologies， the performance of colorectal endoscopy has been significantly improved， effectively helping clinicians make quick and accurate judgments on polyp lesions and formulate appropriate treatment plans. However， in clinical practice polyp segmentation faces numerous challenges， such as different intestinal environments in different patients， and varying sizes and shapes of polyps. To address these challenges and enhance the generalization and learning abilities， the generalization enhancement and dynamic perception network （GEDPNet） is proposed. GEDPNet utilizes the pyramid vision Transformer （PVT_v2） as its backbone and focuses on the design of three key modules： the generalization enhancement （GE） module， the dynamic perception （DP） module， and the cascade aggregation （CA） module. Firstly， the GE module innovatively improves the model’s generalization by extracting the polyp domain-invariant features， effectively alleviating the problem of poor segmentation caused by different intestinal environments of polyps in different patients. Meanwhile， the GE module also addresses the challenge of diverse polyp sizes by extracting rich multi-scale information intra each layer. Secondly， the DP module is able to dynamically perceive the global and local information， and then to effectively capture the position information as well as the boundaries or textures of polyps. Finally， the CA module can fully aggregate multi-scale features at different levels to obtain rich semantic information， ensuring the integrity of polyp information and further enhancing segmentation performance. To verify the effectiveness of the proposed GEDPNet， extensive experiments are conducted on five polyp datasets： Kvasir-SEG， CVC-ClinicDB， CVC-T， CVC-ColonDB， and ETIS. On these five polyp datasets， the mDice of the proposed GEDPNet achieves 0.930， 0.946， 0.911， 0.825， and 0.806， respectively； mIoU of that achieves 0.883， 0.902， 0.848， 0.747， and 0.733， respectively； and MAE of that achieves 0.019， 0.005， 0.005， 0.025， and 0.013， respectively. Furthermore， the proposed GEDPNet has been compared with 20 classical and advanced polyp image segmentation methods and it outperforms nearly all of them. Notably， mIoU of GEDPNet has been improved by 4.3%， 5.3%， 5.1%， 10.7%， and 16.6% respectively on these five polyp datasets compared to that of classical polyp segmentation method PraNet. These results indicate that the proposed GEDPNet exhibits superior dynamic perception capabilities for polyps with significant variations in intestinal environments， sizes， and shapes， so it can effectively enhance the polyp segmentation accuracy and model’s generalization.