The invention relates to the retinal blood vessel segmentation technology and especially relates to a multi-characteristic fusion monitored retinal blood vessel extraction method. The method comprises steps of step 1, retinal blood vessel image pre-processing; step 2, retinal blood vessel image characteristic extraction; step 3, random forest classifier training; and step 4, retina retinal blood vessel image post-processing. The method is advantaged in that through experiment verification of a DRIVE and STARE eyeground image database, susceptibilities are respectively 0.8354 and 0.8452, accuracies are respectively 94.82% and 95.34%, compared with existing retinal blood vessel segmentation methods in the prior art, the multi-characteristic fusion monitored retinal blood vessel extraction method is integrally better, moreover, disadvantages of the other methods at adjacent blood vessel portions, blood vessel cross sections and capillaries are solved, and segmented blood vessel structures are relatively closer to gold standards and actual blood vessel dimension values.