To address the challenges prevalent in polyp segmentation, such as co-occurrence phenomena, boundary ambiguity, and under-segmentation, a novel multi-scale attention fusion network, MSBRAU-Net++, is proposed. It adopts multi-scale gated attention fusion to create an interactive structure between multi-scale features and attention, processing contextual information to enhance foreground feature responses and suppress background interference, thereby significantly improving the ability to distinguish polyps from similar tissues. By utilizing a hybrid spatial channel module, it addresses the issue of boundary ambiguity through deep feature calibration and local detail recovery, thereby enhancing the precision of edge segmentation. A novel multipath feature aggregation block is designed to fuse low-level details with high-level semantic features, preventing information loss and ensuring the integrity of the segmentation results. MSBRAU-Net++ was evaluated on the Kvasir-SEG and CVC-ClinicDB datasets, achieving IoU scores of 84.65% and 88.87%, and DSC scores of 90.63% and 91.99%, respectively. The experimental results demonstrate that MSBRAU-Net++ outperforms other comparative models and can accurately segment images, showing particularly significant results in segmenting regions with complex boundaries and small polyps.