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Accurate segmentation of colorectal polyps is crucial for the early prevention and diagnosis of colorectal cancer. However, due to the high heterogeneity of polyps in terms of shape, size, and texture, as well as the complexity of the intestinal environment (such as folds, specular reflections, and fecal residues), existing methods still face significant challenges in boundary localization and small-polyp detection. To address these issues, this paper proposes a Polyp Segmentation Network based on Pinwheel Convolution and Dual Attention (PWD-Net). The proposed network adopts a U-shaped encoder-decoder architecture, where a pretrained ResNet is employed as the encoder to extract multi-level local features. Specifically, a Pinwheel Convolution Module (PCM) is introduced at the bottleneck layer to capture the global geometric structure and multi-directional contextual information of polyps through multi-angle rotated convolution kernels. A Dual-Attention Mechanism (DAM) that integrates channel attention and spatial attention is designed to adaptively suppress background noise and enhance polyp-region features. In addition, a Multi-scale Feature Fusion (MSF) strategy is employed to combine deep semantic information with shallow boundary details, ensuring both completeness and precision of segmentation results. Experiments conducted on the Kvasir-SEG and CVC-ClinicDB datasets demonstrate that PWD-Net achieves average Dice coefficients of 0.865 and 0.944, and IoU scores of 0.765 and 0.892, respectively, significantly outperforming existing state-of-the-art methods. Ablation studies verify the effectiveness of each module, and cross-dataset evaluations confirm the strong generalization ability of the model. This study provides a high-precision and robust solution for clinical polyp segmentation, offering significant value for the early diagnosis of colorectal precancerous lesions and supporting computer-aided intervention.