Aiming at the complex and changeable morphological structure of retinal blood vessels, this study proposes a retinal blood vessel segmentation algorithm based on multidirectional filtering to solve the problem that the intersection and extension in blood vessel images are not easy to segment. First, the green channel of the retinal blood vessel image is selected by using histogram equalization, median filter denoising, top hat transformation, and other methods for image enhancement. Then, multidirectional Cake filtering is performed on the enhanced image. The filtered results are fused to weaken the noise in the background and enhance the contrast between the blood vessel and background. Finally, the vector field divergence method is used to extract the threshold, and the image is segmented to obtain the final retinal vessel segmentation result. The algorithms are tested on the public DRIVE and STARE datasets. The experimental results show that the proposed algorithm is simple and effective and adapts to the complex and changeable characteristics of retinal blood vessel scale information. The proposed algorithm can also handle the junction of complex blood vessels and has higher sensitivity, shorter execution time, and higher efficiency than other algorithms.