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.　©　2018　IEEE.