To segment target regions in retinal blood vessel images more accurately, a network based on improved W-Net is proposed. The network uses diamond structure fusion for semantic feature aggregation by stacking the parts containing the diamond structure layer by layer to form a U-shaped widening framework and introducing nested dense jump connections to form the final model. The fusion scheme improves the flexibility of feature map combination, and the designed jump connections reduce the semantic gap between feature maps, thus reducing the learning pressure on the optimizer and achieving better image segmentation performance. The effectiveness of the proposed network is verified using the DRIVE dataset. The dice similarity coefficient values, sensitivity, specificity, and accuracy obtained by BW-Net in the segmentation task are 76.86%, 73.66%, 99.12%, and 94.55%, respectively, which perform better than the output of most of the current state-of-the-art network frameworks, and the network parameters are reduced. The results demonstrate the improvement of this extended structure in the performance of retinal vascular image segmentation.