Cataract　is　one　of　the　most　serious　eye　diseases　leading　to　blindness.　Early　detection　and　treatment　can　reduce　the　rate　of　blindness　in　cataract　patients.　However,　the　professional　knowledge　of　ophthalmologists　is　necessary　for　the　clinical　cataract　detection.　Therefore,　the　potential　costs　may　make　it　difficult　for　the　widespread　use　of　cataract　detection　to　prevent　blindness.　Artificial　intelligence　assisted　diagnosis　based　on　medical　images　has　attracted　more　and　more　attention　of　researchers.　Many　studies　have　focused　on　the　use　of　pre-defined　feature　sets　for　cataract　classification,　but　the　predefined　feature　sets　may　be　incomplete　or　redundant.　On　account　of　the　aforementioned　issues,　some　studies　have　proposed　deep　learning　methods　to　automatically　extract　image　features,　but　all　based　on　global　features　and　none　has　analyzed　the　layer-by-layer　transformation　process　of　the　middle-tier　features.　This　paper　uses　convolutional　neural　networks　(CNN)　to　learn　useful　features　directly　from　input　data,　and　deconvolution　network　method　is　employed　to　investigate　how　CNN　characterizes　cataract　layer-by-layer.　We　found　that　compared　to　the　global　feature　set,　the　detail　vascular　information,　which　is　lost　after　multi-layer　convolution　calculation　also　plays　an　important　role　in　cataract　grading　task.　And　this　finding　fits　with　the　morphological　definition　of　fundus　image.　Through　the　finding,　we　gained　insights　into　the　design　of　hybrid　global-local　feature　representation　model　to　improve　the　recognition　performance　of　automatic　cataract　grading.