Cataract　is　one　of　the　most　common　causes　of　visual　blindness,　about　90%　of　the　elderly　over　60　years　old　with　visual　impairment　in　China　have　cataract　diseases,　and　about　90%　of　eye　diseases　are　diagnosed　by　observing　the　fundus.　The　observation　of　fundus　has　always　been　a　necessary　mean　of　diagnosing　a　cataract.　Moreover,　it　is　highly　uncertain　about　judging　the　degree　of　lesions　based　on　experience,　but　also　the　efficiency　of　this　method　is　very　low.　Therefore,　employing　a　computer-aided　diagnostic　system　to　perform　the　automatic　grading　of　cataract　is　of　great　research　value　of　practical　use.　Most　of　the　studies　reported　in　the　literature　utilize　histogram　equalization　(Histeq)　or　other　image　enhancement　methods　based　on　gray　value　changes　to　improve　the　contrast.　In　this　paper,　the　adaptive　window　model　(AWM)　is　used　to　enhance　the　contrast　between　the　vessel　and　the　background.　We　used　features　extracted　from　the　spoke　features　of　the　image　for　cataract　grading.　The　best　average　accuracy　achieved　by　Support　Vector　Machine　(Back　Propagation　Neural　Network)　is　80.12%　(78.26%)　when　AWM　is　used　to　enhance　the　contrast.　Furthermore,　it　is　even　higher　than　73.29%　(75.16%)　when　the　Histeq　is　used　as　an　image　enhancement　technique.