Diabetic　Retinopathy　(DR)　is　a　universal　ocular　complication　of　diabetes　patients　and　also　the　main　disease　that　causes　blindness　in　the　world　wide.　Automatic　and　efficient　DR　grading　acts　a　vital　role　in　timely　treatment.　However,　it　is　difficult　to　effectively　distinguish　different　types　of　distinct　lesions　(such　as　neovascularization　in　proliferative　DR,　microaneurysms　in　mild　NPDR,　etc.)　using　traditional　convolutional　neural　networks　(CNN),　which　greatly　affects　the　ultimate　classification　results.　In　this　article,　we　propose　a　triple-cascade　network　model　(Triple-DRNet)　to　solve　the　aforementioned　issue.　The　Triple-DRNet　effectively　subdivides　the　classification　of　five　types　of　DR　as　well　as　improves　the　grading　performance　which　mainly　includes　the　following　aspects:　(1)　In　the　first　stage,　the　network　carries　out　two　types　of　classification,　namely　DR　and　No　DR.　(2)　In　the　second　stage,　the　cascade　network　is　intended　to　distinguish　the　two　categories　between　PDR　and　NPDR.　(3)　The　final　cascade　network　will　be　designed　to　differentiate　the　mild,　moderate　and　severe　types　in　NPDR.　Experimental　results　show　that　the　ACC　of　the　Triple-DRNet　on　the　APTOS　2019　Blindness　Detection　dataset　achieves　92.08%,　and　the　QWK　metric　reaches　93.62%,　which　proves　the　effectiveness　of　the　devised　Triple-DRNet　compared　with　other　mainstream　models.