In colonoscopy, polyp automatic detection and image segmentation are key technologies to reduce the incidence rate of colon cancer and improve the survival rate of patients. The goal of this study is to develop a new deep-learning algorithm to improve the accuracy of automatic detection and segmentation of polyp images in colonoscopy, thereby contributing to the early detection and diagnosis of colon cancer and ultimately improving patient survival. To address the challenges of polyp image segmentation, this paper proposes a deep-learning algorithm named Gaussian error linear unit omni-dimensional dynamic convolution U-Net (referred to as GODC-U-Net). This algorithm is based on the U-Net network structure and integrates dynamic convolution and parallel multidimensional attention mechanisms to effectively learn the global and local feature information of polyp images. A hybrid loss function and a series of improvements to U-Net were introduced to further optimize model performance. Evaluation results on publicly available polyp segmentation benchmark datasets such as Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, and ETIS-AribPolyDB show that proposed method achieved advanced levels in terms of the Dice coefficient, intersection over union index, accuracy, recall, and accuracy. This algorithm demonstrates excellent generalizability and high performance under limited training data in addressing polyp image segmentation problems, thus providing effective technical support for the early detection and diagnosis of colon cancer.