This study proposes a vascular segmentation network that integrates multiscale feature and a dual attention mechanism to address low segmentation accuracy caused by unsatisfactory segmentation of small retinal vessels and poor vascular connectivity. First, the dilated residual module with introduction of the dual attention mechanism is used to replace the original convolutional layer of U-Net, achieving multiscale extraction of vascular features. Second, a feature fusion module is embedded in the skip connections, reducing information loss during the encoding-decoding process and enhancing vascular connectivity through the adaptive fusion of vascular information. Finally, a hybrid loss function is introduced to assist network training, alleviating the class imbalance problem in retinal vascular images. Experimental results on the DRIVE and CHASE_DB1 datasets demonstrate that the proposed algorithm achieves an accuracy of 0.9625 and 0.9696, respectively. Compared with U-Net, the sensitivity of the proposed algorithm increased by 0.0420 and 0.0552, and the F1 score increased by 0.0140 and 0.0342, demonstrating improved segmentation performance.