Retinal vascular abnormalities are key diagnostic indicators for various diseases, including glaucoma, cataracts, hypertension, diabetes, and arteriosclerosis. Due to the complex structure of retinal images, low contrast between blood vessels and the background, and blurred microvascular structures, retinal vessel segmentation is highly challenging. To tackle these issues, this paper introduces a multi-scale attention-guided fusion network (FPGS-Net), which aims to enhance the accuracy of automatic segmentation. The network consists of a multi-scale feature convolution module, which alleviates the semantic gap between vessels and the background and enhances noise suppression, thus preserving microvascular features. The Fusion Pooling Module (FPM) enhances the model’s ability to capture microvessels by integrating multi-scale features. It preserves local details while ensuring the completeness of global semantic information. Additionally, a guided attention skip module (GSM) combines attention mechanisms with feature fusion strategies to resolve semantic discrepancies between shallow and deep features, improving global feature extraction and structural integrity. To address the class imbalance of vascular pixels, to enhance the network’s focus on vascular regions during training, a hybrid loss function is proposed, integrating the Dice coefficient and the Focal Tversky coefficient. Experimental results on the DRIVE, CHASE_DB1, and STARE datasets demonstrate that FPGS-Net outperforms existing state-of-the-art methods, achieving superior segmentation performance and validating the model’s effectiveness.