Accurate segmentation of colorectal polyps can effectively prevent the incidence of colorectal cancer. Current segmentation methods primarily rely on spatial domain analysis with insufficient frequency information utilization. This spatial-centric approach suffers from locality constraints and limited sensitivity to subtle polyp variations. Furthermore, existing techniques often exhibit inadequate information transmission and fusion across multi-scale features. To address these issues, we propose DCSFNet, a colon polyp segmentation method based on joint spatial-frequency domain perception and deeply coupled feature interaction, which includes four key components: multi-scale feature reconstruction optimization module (MROM), dual-domain group learning module (DGLM), dual-domain interactive perception module (DIPM) and multi-scale feature global perception aggregation module (MGPAM). Specifically, MROM is introduced to reconstruct encoder features and progressively transmit the local fine details of polyps, mitigating information loss in high-level semantics. DGLM is designed to simultaneously enhance local structural details and global contextual understanding by exploiting complementary cues from both spatial and frequency domains. Furthermore, DIPM is present to facilitate effective interaction and complementary learning between spatial and frequency domain features, providing richer and more comprehensive information for segmentation. Finally, MGPAM is proposed to aggregate multi-scale features to capture richer contextual interdependencies. Comprehensive experiments demonstrate that DCSFNet achieves superior segmentation performance across five benchmark polyp datasets.