In addressing issues such as feature redundancy in traditional U-shaped networks and the complexity of retinal vascular morphology, as well as challenges in segmenting fine blood vessels, this study proposes a multi-flow retinal vascular segmentation algorithm based on improved U-Net. The algorithm incorporates two feature flows, a global segmentation flow and a boundary-specialized flow. To reduce feature redundancy, the global segmentation flow replaces the traditional U-Net convolution block with a fast extraction module based on partial convolution and constructs an improved U-Net model that can efficiently extract vascular features and accelerate algorithm inference speed. To minimize noise interference and enhance the segmentation accuracy of fine blood vessels, the boundary-specialized flow utilizes morphologically generated boundary annotations as guidance. Multiple boundary extraction modules, in combination with the high-level semantic features from the global segmentation flow and boundary attention, are employed to more selectively extract vascular details, thereby strengthening the feature representation of fine blood vessels. The effectiveness of the algorithm is evaluated on the DRIVE and STARE datasets, yielding sensitivity values of 0.841 5 and 0.836 9, accuracy values of 0.970 1 and 0.971 8, and AUC values of 0.987 7 and 0.990 9, respectively. The overall performance surpassed that of existing algorithms.