Retinal blood vessel segmentation is an important means to detect a variety of eye diseases, and it plays an important role in automatic screening systems for retinal diseases. Aiming at the problems of insufficient segmentation of small blood vessels and pathological mis-segmentation by existing methods, a segmentation algorithm based on the multi-scale attention analytic network is proposed. The network is based on the encoding-decoding architecture and introduces attention residual blocks in sub-modules, therefore enhancing the feature propagation ability and reducing the effects of uneven illumination and low contrast on the model. The jump connection is added between the encoder and decoder and the traditional pooling layer is removed to retain sufficient blood vessel detail information. Two multi-scale feature fusion methods, parallel multi-branch structure and spatial pyramid pooling, are used to achieve feature extraction under different receptive fields and improve the performance of blood vessel segmentation. Experimental results show that the F₁ value of this method on the CHASEDB1 and STARE standard sets reaches 83.26% and 82.56%, the sensitivity reaches 83.51% and 81.20%, respectively, and the proposed method is better than that of current mainstream methods.