Cataract　is　one　of　the　most　prevalent　causes　of　blindness　in　the　industrialized　world,　accounting　for　more　than　50%　of　blindness.　Early　detection　and　treatment　can　reduce　the　suffering　of　cataract　patients　and　prevent　visual　impairment　from　turning　into　blindness.　But　the　expertise　of　trained　eye　specialists　is　necessary　for　clinical　cataract　detection　and　grading,　which　may　cause　difficulties　to　everybody＇s　early　intervention　due　to　the　underlying　costs.　Existing　studies　on　automatic　cataract　detection　and　grading　based　on　fundus　images　utilize　a　predefined　set　of　image　features　that　may　provide　an　incomplete,　redundant,　or　even　noisy　representation.　This　paper　aims　to　investigate　the　performance　and　efficiency　by　using　Depp　Convolutional　Neural　Network　(DCNN)　to　detect　and　grad　cataract　automatically,　it　also　visualize　some　of　the　feature　maps　at　pool5　layer　with　their　high-order　empirical　semantic　meaning,　providing　a　explanation　to　the　feature　representation　extracted　by　DCNN.　The　proposed　DCNN　classification　system　is　cross　validated　on　different　number　of　population-based　clinical　retinal　fundus　images　collected　from　hospital,　up　to　5620　images.　There　are　two　conclusions　suggested　in　this　paper:　The　first　one　is,　the　interference　of　local　uneven　illumination　and　the　reflection　of　eyes　were　overcome　by　using　the　retinal　fundus　images　after　G-filter,　which　makes　an　significant　contribution　to　DCNN　classification.　The　second　one　is,　with　the　increase　of　the　amount　of　available　samples,　the　DCNN　classification　accuracies　are　increasing,　and　the　fluctuation　range　of　accuracies　are　more　stable.　The　best　accuracy,　our　method　achieved,　is　93.52%　and　86.69%　in　cataract　detection　and　grading　tasks　separately.　It　is　demonstrated　in　this　paper　that　the　DCNN　classifier　outperforms　state-of-the-art　in　the　performance.　Further　more,　The　proposed　method　has　the　potential　to　be　applied　to　other　eye　diseases　in　future.　©　2017　IEEE.