Deep　learning　technology　has　played　an　important　role　in　our　life.　Since　deep　learning　technology　relies　on　the　neural　network　model,　it　is　still　plagued　by　the　catastrophic　forgetting　problem,　which　refers　to　the　neural　network　model　will　forget　what　it　has　learned　after　learning　new　knowledge.　The　neural　network　model　learns　knowledge　through　labeled　samples,　and　its　knowledge　is　stored　in　its　parameters.　Therefore,　many　methods　try　to　solve　this　problem　from　the　perspective　of　constraint　parameters　and　stored　samples.　There　are　few　ways　to　solve　this　problem　from　the　perspective　of　constraining　features　output　of　neural　network　models.　This　paper　proposes　an　incremental　learning　method　with　super　constraints　on　model　parameters.　This　method　not　only　calculates　the　parameter　similarity　loss　of　the　old　and　new　models,　but　also　calculates　the　layer　output　feature　similarity　loss　of　the　old　and　new　models,　and　finally　suppresses　the　change　of　model　parameters　from　two　directions.　In　addition,　we　also　propose　a　new　strategy　for　selecting　representative　samples　from　dataset　and　tackling　the　imbalance　between　stored　samples　and　new　task　samples.　Finally,　we　utilize　the　neural　kernel　mapping　support　vector　machine　theory　to　increase　the　interpretability　of　the　model.　In　order　to　better　meet　the　actual　situation,　five　sample　sets　with　different　categories　and　amounts　were　employed　in　experiments.　Experiments　show　the　effectiveness　of　our　method.　For　example,　after　learning　the　last　task,　our　method　is　at　least　1.930%　and　0.562%　higher　than　other　methods　on　the　training　set　and　test　set,　respectively.