In order to provide effective foundation for retinal image registration, illumination adjustment and pathological detection of retina interior and other problems, a fully automatic method of detecting and recognizing blood vessel for color retina fundus images effectively was proposed. Aimed at the state with elongated tubular shape and preferably linear structure in local part of visible blood vessel, combinatorial shifted filter response model that is applicable to strip structure was used for feature extraction. Taking different features of blood vessel and the end of blood vessel into consideration, two types of filtering modes with symmetry and dissymmetry were configured for tracking, feature vector library was established by response acquired from combinatorial shifting filter response model (symmetry and dissymmetry) and G channel pixel value together and each pixel was classified and determined by AdaBoost classifier. The experimental result based on international public database DRIVE and STARE shows that the segmentation result of proposed method on two standard databases (DRIVE: Accuracy=0.948 9, Sensitivity=0.765 7, Specificity=0.980 9; STARE: Accuracy=0.956 7, Sensitivity=0.771 7, Specificity=0.976 6) is better than existing methods. It is applicable to computer-assisted quantitative analysis of color retina fundus images and can be used as clinical reference.