Nowadays,　with　the　rapid　development　of　industry,　people＇s　daily　life　is　becoming　more　and　more　rich　and　diverse.　However,　while　enjoying　the　pleasure　brought　by　material　life,　a　large　number　of　garbage　are　produced.　They　are　various,　and　people　lack　the　knowledge　related　to　garbage　classification,　which　leads　to　the　difficulty　of　manual　or　robot　classification.　This　paper　studies　a　garbage　classification　algorithm　model　based　on　deep　learning　convolutional　neural　network　Efficientnet　to　help　identify　garbage　classification.　In　this　research,　data　augmentation　and　normalization　are　carried　out　to　solve　the　problem　of　small　amount　of　data　sets　and　different　sizes　of　pictures.　Efficientnet　is　used　to　extract　the　features　of　images.　In　order　to　solve　the　problem　that　BN　has　no　obvious　effect　on　small　batches　in　the　network,　we　replace　BN　with　group　normalization　(GN).　In　order　to　prevent　some　irrelevant　information　in　the　image　from　affecting　the　training　of　the　model,　we　add　attention　mechanism　after　the　output　of　Efficientnet　to　emphasize　or　select　the　important　information　of　the　target　processing　object,　and　suppress　some　irrelevant　details,　so　that　the　model　can　focus　on　the　key　features　and　better　identify　the　image;　according　to　the　above　process,　we　use　softmax　to　classify　the　spam　image　and　divide　it　into　four　categories　(Recyclables,　Kitchen　garbage,　Hazardous　garbage,　Other　garbage)　The　results　show　that　the　model　can　effectively　extract　the　features　of　the　input　garbage　image,　and　get　accurate　judgment,　and　identify　the　types　of　garbage.　The　experimental　results　show　that　the　average　accuracy　of　the　algorithm　model　is　high,　and　has　good　classification　performance　and　robustness.　In　the　practical　significance　of　the　research,　this　reliable　model　can　help　people　quickly　know　the　type　of　garbage,　or　can　be　applied　to　robot　sorting,　to　help　detect　the　types　of　garbage　for　robot　judgment　and　sorting,　so　it　has　very　important　application　scenarios　and　significance.　©　2021　IEEE.