Tongue　color　is　one　of　the　most　concerned　diagnostic　features　of　tongue　diagnosis　in　Traditional　Chinese　Medicine　(TCM).　Automatic　and　accurate　tongue　color　classification　is　an　important　content　of　the　objectification　of　tongue　diagnosis.　Due　to　the　vagueness　of　the　visual　boundaries　between　different　types　of　tongue　colors　and　the　subjectivity　of　the　doctors,　the　annotated　tongue　image　data　samples　often　contain　noises,　which　has　a　negative　effect　on　the　training　of　the　tongue　color　classification　model.　Therefore,　in　this　paper,　a　tongue　color　classification　method　in　TCM　with　noisy　labels　is　proposed.　Firstly,　a　two-stage　data　cleaning　method　is　proposed　to　identify　and　clean　noisy　labeled　samples.　Secondly,　a　lightweight　Convolutional　Neural　Network　(CNN)　based　on　the　channel　attention　mechanism　is　designed　in　this　paper　to　achieve　accurate　classification　of　tongue　color　by　enhancing　the　expressiveness　of　features.　Finally,　a　knowledge　distillation　strategy　with　a　noise　sample　filtering　mechanism　is　proposed.　This　strategy　adds　a　noise　sample　filtering　mechanism　led　by　the　teacher　network　to　eliminate　further　noise　samples.　At　the　same　time,　the　teacher　network　is　used　to　guide　the　training　of　the　light　convolutional　neural　network　to　improve　the　classification　performance.　The　experimental　results　on　the　self-established　TCM　tongue　color　classification　dataset　show　that　the　proposed　method　in　this　paper　can　significantly　improve　the　classification　accuracy　with　lower　computational　complexity,　reaching　93.88%.