In　the　traditional　review　text　classification　method,　in　order　to　realize　the　high　accuracy　of　classification　model,　there　are　two　basic　premises:　(1)　training　data　and　test　data　must　be　distributed　independently　and　uniformly;　(2)　there　must　be　enough　training　data　to　learn　a　good　classification　model.　However,　in　many　cases,　these　two　premises　are　not　true.　If　a　classification　model　already　exists　and　classifies　data　from　a　domain　well,　then　a　classification　task　for　a　related　domain　exists,　but　only　data　from　the　source　domain,　then　it　may　violate　this　assumption.　The　comment　text　classification　method　based　on　transfer　learning　refers　to　applying　the　classification　knowledge　learned　in　the　source　domain　to　the　new　classification　task　in　the　relevant　field　by　using　the　transfer　learning　method　in　the　process　of　classifying　the　comment　text.　Therefore,　after　constructing　the　isomorphic　feature　space　of　source　domain　and　target　domain,　the　TrAdaBoost　migration　learning　framework　was　used　to　train　the　classification　model.　This　model　allows　users　to　leverage　old　data　with　a　small　amount　of　new　markup　data　to　build　a　high-quality　classification　model　for　new　data.　Experimental　results　show　that　the　model　can　effectively　transfer　classification　knowledge　from　source　domain　to　target　domain.　©　2020　IEEE.