Auto　tongue　color　classification　is　an　important　research　topic　in　the　study　of　TCM　(Traditional　Chinese　Medicine)　objectification.　Affected　by　various　factors　such　as　doctor’s　experience　and　illumination　conditions,　there　often　exist　errors　in　the　manually　annotated　labels,　that　is,　noisy　labels.　Noisy　labels　will　cause　the　model　not　to　converge　in　the　training　process　and　the　generalization　ability　will　be　poor.　Therefore,　in　this　paper,　a　TCM　tongue　color　classification　method　is　proposed　by　progressively　correcting　noisy　samples.　First,　according　to　the　characteristics　of　the　tongue　color　classification,　a　global-local　feature　fusion　method　is　proposed,　which　is　embedded　in　the　ResNet18　backbone　network,　constructing　a　tongue　color　classification　network.　The　ensemble　learning　paradigm　is　adopted　to　improve　the　reliability　and　stability　of　the　classification　model.　Next,　for　the　classification　network　training　problem　under　noisy　samples,　a　sample　attention　mechanism　and　a　re-labeling　mechanism　are　proposed.　During　the　training　process,　different　weights　are　assigned　to　clean　samples　and　noisy　samples,　and　the　noisy　samples　are　gradually　adjusted.　Finally,　the　network　model　is　optimized　and　trained　with　the　Boostrapping　loss　function　to　suppress　the　impact　of　noisy　samples　on　the　classification　performance.　The　experimental　results　on　two　tongue　color　classification　datasets　SIPL-A　and　SIPL-B　show　that,　the　proposed　method　can　effectively　correct　noisy　labels,　thereby,　significantly　improving　the　tongue　color　classification　accuracy.　Compared　with　the　existing　image　classification　methods　under　noisy　samples,　the　proposed　method　can　achieve　a　higher　classification　accuracy,　reaching　94.6%　and　93.65%,　respectively.　©　2024　Chinese　Institute　of　Electronics.　All　rights　reserved.