Tongue　color　is　an　important　tongue　diagnostic　index　for　traditional　Chinese　medicine　(TCM).　Due　to　the　individual　experience　of　TCM　experts　as　well　as　ambiguous　boundaries　among　the　tongue　color　categories,　there　often　exist　noisy　labels　in　annotated　samples.　Deep　neural　networks　trained　with　the　noisy　labeled　samples　often　have　poor　generalization　capability　because　they　easily　overfit　on　noisy　labels.　A　novel　framework　named　confident-learning-assisted　knowledge　distillation　(CLA-KD)　is　proposed　for　tongue　color　classification　with　noisy　labels.　In　this　framework,　the　teacher　network　plays　two　important　roles.　On　the　one　hand,　it　performs　confident　learning　to　identify,　cleanse　and　correct　noisy　labels.　On　the　other　hand,　it　learns　the　knowledge　from　the　clean　labels,　which　will　then　be　transferred　to　the　student　network　to　guide　its　training.　Moreover,　we　elaborately　design　a　teacher　network　in　an　ensemble　manner,　named　E-CA(2)-ResNet18,　to　solve　the　unreliability　and　instability　problem　resulted　from　the　insufficient　data　samples.　E-CA(2)-ResNet18　adopts　ResNet18　as　the　backbone,　and　integrates　channel　attention　(CA)　mechanism　and　activate　or　not　activation　function　together,　which　facilitates　to　yield　a　better　performance.　The　experimental　results　on　three　self-established　TCM　tongue　datasets　demonstrate　that,　our　proposed　CLA-KD　can　obtain　a　superior　classification　accuracy　and　good　robustness　with　a　lower　network　model　complexity,　reaching　94.49%,　92.21%,　93.43%　on　the　three　tongue　image　datasets,　respectively.