Objective　To　automatically　extract　the　characteristics　of　fundus　cataract　by　deep　learning,　construct　a　automatic　classifier　for　cataract,　and　visualize　the　layer-by-layer　feature　transformation　process　of　the　intermediate　layer　of　deep　network.　Methods　Based　on　the　clinical　fundus　image,　a　deep　convolutional　neural　network　(CNN)　was　used　to　directly　learn　useful　features　from　the　original　representation　of　input　data,　and　then　the　features　extracted　by　the　CNN　were　compared　with　pre-defined　features.　The　deconvolution　neural　network　(DN)　method　was　used　to　quantitatively　analyze　the　characteristics　of　each　intermediate　layer　of　CNN,　analyze　the　pixel　sets　that　have　the　most　contribution　to　the　prediction　performance　of　CNN　in　the　input　image,　and　explore　the　process　in　characterizing　cataract　by　CNN.　Results　The　classifier　constructed　by　deep　learning　achieved　an　average　accuracy　of　0.818　6　in　four-category　tasks.　Compared　with　the　existing　predefined　feature　set,　the　feature　set　automatically　extracted　by　the　deep　CNN　performed　better　in　representing　characteristics　of　cataract.　The　features　of　the　intermediate　layer　of　CNN　hierarchically　transformed　from　low-level　abstraction　to　high-level　abstraction,　including　changed　from　gradient　to　edge,　then　to　the　combination　of　edge-like　divergent　structures,　and　finally　to　the　high-level　abstraction　of　blood　vessel　and　optic　disc　information,　and　this　transformation　process　coincided　with　the　clinical　diagnostic　criteria　of　cataract.　Conclusion　The　classifier　based　on　deep　learning　is　superior　to　the　existing　classifier　in　terms　of　performance.　In　addition,　this　method　has　potential　application　in　detecting　other　eye　diseases.　©　2018,　Second　Military　Medical　University　Press.　All　rights　reserved.