The variability in size, shape, color, and texture, along with the blurring demarcation of the bowel wall, presents a significant challenge in colon polyp segmentation. The detail information loss and lack of interaction between different feature levels due to continuous sampling in single-branch networks lead to poor segmentation results. To address this problem, this study proposes a two-branch colon polyp segmentation network based on local-global feature interaction. The network utilizes a dual branch structure consisting of CNN and Transformer, systematically capturing the precise local details and the global semantic features of the polyp in each layer. To make full use of the complementary nature of feature information at different levels and scales, and to utilize the guidance and enhancement of shallow detailed features by deep semantic features, the paper designs the feature cooperative interaction module to dynamically sense and aggregate cross-level feature interaction information. To enhance the feature of the polyp lesion region while reducing background noise, the feature enhancement module utilizes spatial and channel attention mechanisms. Additionally, the skip-connection mechanism in conjunction with the attention gate further highlights boundary information, resulting in improved edge region segmentation accuracy. Experiments show that the proposed network achieves better mDice and mIoU scores than the baseline network on multiple polyp segmentation datasets, with higher segmentation accuracy and stability.