Retinal fundus image can perceive deep-seated blood vessels in the human body in a non-invasive manner. Retinal blood vessels are the primary anatomical structure that can be visible in the fundus image, while changes in the structural feature of retinal blood vessels cannot only reflect all sort of pathological changes but also serve as an important evidence for diagnosing cataract and other diseases. Automatic fundus image processing and analyzing in the computer has a significant effect on the auxiliary medical diagnosis. Moreover, the blood vessels extracted can be used as a feature for the classification of cataract fundus images. Most of the blood vessel extraction methods often used a heuristic feature set that are usually be extracted manually. For the limitations of current methods, we propose to use deep learning to identify blood vessels, which can perform automatic feature learning. We collected the dataset containing fundus images of 5620 patients for the extraction of blood vessels. We then performed Preprocessing by extracting green channel components and histogram equalization. We also present FCN structure in the fusion of dual sources in which preprocessed grayscale image and the edge information processed by the Sobel operators are used as an input. We also document that FCN enhance the richness of the input features and improve the accuracy. It can be concluded that the proposed method achieves the optimal accuracy for recognizing blood vessels of patients with cataract. Moreover, the accuracy of extracting normal fundus vessels reaches 94.91%.Furthermore, we are intended to use this proposed method for the vascular identification of other medical images.