Cataracts　are　the　most　crucial　cause　of　blindness　among　all　ophthalmic　diseases.　Convenient　and　cost-effective　early　cataract　screening　is　urgently　needed　to　reduce　the　risks　of　visual　loss.　To　date,　many　studies　have　investigated　automatic　cataract　classification　based　on　fundus　images.　However,　existing　methods　mainly　rely　on　global　image　information　while　ignoring　various　local　and　subtle　features.　Notably,　these　local　features　are　highly　helpful　for　the　identification　of　cataracts　with　different　severities.　To　avoid　this　disadvantage,　we　introduce　a　deep　learning　technique　to　learn　multilevel　feature　representations　of　the　fundus　image　simultaneously.　Specifically,　a　global-local　attention　network　(GLA-Net)　is　proposed　to　handle　the　cataract　classification　task,　which　consists　of　two　levels　of　subnets:　the　global-level　attention　subnet　pays　attention　to　the　global　structure　information　of　the　fundus　image,　while　the　local-level　attention　subnet　focuses　on　the　local　discriminative　features　of　the　specific　regions.　These　two　types　of　subnets　extract　retinal　features　at　different　attention　levels,　which　are　then　combined　for　final　cataract　classification.　Our　GLA-Net　achieves　the　best　performance　in　all　metrics　(90.65%　detection　accuracy,　83.47%　grading　accuracy,　and　81.11%　classification　accuracy　of　grades　1　and　2).　The　experimental　results　on　a　real　clinical　dataset　show　that　the　combination　of　global-level　and　local-level　attention　models　is　effective　for　cataract　screening　and　provides　significant　potential　for　other　medical　tasks.