Deep　neural　network　(DNN)　based　on　incremental　learning　provides　support　for　efficient　garbage　classification　tasks.　However,　it　is　always　challenging　to　accurately　learn　and　preserve　the　information　of　known　classes　for　updating　DNN　while　new　tasks　are　continuously　emerging,　which　also　affects　the　generalization　performance　of　the　model.　To　solve　these　issues,　an　incremental　evolution　learning　(IEL)　method　based　on　prototype　enhancement　is　proposed　to　accurately　preserve　data　and　improve　the　model　generalization　ability.　First,　a　prototype　enhancement　method　based　on　multi-dimensional　Gaussian　kernel　density　estimation　is　designed,　which　extends　the　prototype　of　each　sample　based　on　high-dimensional　nonlinear　data　distribution.　Then,　the　prototype　enhancement　accurately　represents　the　known　class　data.　Second,　a　contrastive　feature　method　is　proposed　to　constrain　the　consistency　of　features　between　tasks,　which　reduces　the　deviation　between　different　tasks.　Then,　the　extraction　preference　caused　by　the　class　sample　imbalance　is　balanced　and　the　generalization　ability　is　improved.　Third,　the　proposed　IEL　is　used　for　garbage　classification　with　imbalanced　class　samples.　IEL　implements　garbage　classification　by　effectively　adapting　to　the　differences　between　known　classes　and　new　classes.　Finally,　experiments　on　four　standard　datasets　and　one　public　garbage　dataset　verify　that　IEL　has　a　strong　classification　ability　in　the　learning　tasks　with　the　increasing　number　of　classes.　IEEE