The　Grammatical　Error　Correction　(GEC)　task　is　to　realize　automatic　error　detection　and　correction　of　text　through　natural　language　processing　technology,　such　as　word　order,　spelling　and　other　grammatical　errors.　Many　existing　Chinese　GEC　methods　have　achieved　good　results,　but　these　methods　have　not　taken　into　account　the　characteristics　of　learners,　such　as　level,　native　language　and　so　on.　Therefore,　this　paper　proposes　to　personalize　the　GEC　model　to　the　characteristics　of　Chinese　as　a　Second　Language　(CSL)　learners　and　correct　the　mistakes　made　by　CSL　learners　with　different　characteristics.　To　verify　our　method,　we　construct　domain　adaptation　datasets.　Experiment　results　on　the　domain　adaptation　datasets　demonstrate　that　the　performance　of　the　GEC　model　is　greatly　improved　after　adapting　to　various　domains　of　CSL　learners.　©　2020　China　National　Conference　on　Computational　Linguistics　Published　under　Creative　Commons　Attribution　4.0　International　License