Cataract　is　defined　as　a　lenticular　opacity　presenting　usually　with　poor　visual　acuity.　It　is　considered　the　most　common　cause　of　blindness.　Early　diagnosis　and　treatment　can　reduce　the　suffering　of　patients　and　prevent　visual　impairment　from　turning　into　blindness.　Recently,　cataract　diagnosis　applying　pattern　recognition　is　in　a　rising　period.　For　retinal　fundus　images,　the　task　is　usually　cataract　classification.　However,　it　needs　complex　manual　processing,　which　demands　dexterous　people　and　time　taking　exertion.　Besides,　it　faces　the　challenge　of　effective　interpretability　and　dependability.　In　this　paper,　we　develop　a　deep-learning　algorithm　to　intuitively　identify　cataract　attributes　to　solve　these　limitations.　Our　model,　is　a　18(50)-layer　convolutional　neural　network　that　inputs　retinal　fundus　images　in　G　channel　and　outputs　the　prediction　with　heatmap.　The　heatmap　localizes　the　areas　where　most　indicative　of　different　levels　of　cataract　is.　Furthermore,　we　extend　the　training　strategy　for　the　corresponding　task,　which　aims　at　improving　the　performance　of　the　network.　Comparing　with　other　methods　in　cataract　classification,　we　succeeded　to　achieve　state　of　the　art　accuracy　of　proposed　method　on　detection　and　grading　task.　Most　importantly,　our　model　provides　a　compelling　reason　via　localizing　the　areas　revealing　cataract　in　the　image.