Tongue　color　is　one　of　the　most　concerning　diagnostic　features　of　tongue　diagnosis　in　Traditional　Chinese　Medicine　(TCM).　In　practical　applications,　the　performance　of　the　model　trained　from　the　tongue　data　acquired　by　one　device　is　dramatically　degraded　when　applied　to　other　devices　due　to　the　data　distribution　discrepancy.　Therefore,　in　this　paper,　a　few　shot　domain　adaptation　tongue　color　classification　method　with　two-stage　meta-learning　is　proposed.　Firstly,　a　two-stage　meta-learning　training　strategy　is　proposed　to　extract　domain　invariant　features　from　labeled　samples　in　the　source　domain,　and　then,　the　meta-trained　network　model　is　fine-tuned　using　a　few　labeled　data　in　the　target　domain,　so　that　the　model　can　quickly　adapt　to　the　new　sample　characteristics　in　the　target　domain,　improving　the　generalization　ability　of　the　tongue　color　classification　model　and　avoid　overfitting　problem.　Next,　a　progressive　pseudo　label　generation　strategy　is　proposed,　which　uses　the　meta-trained　model　to　predict　the　unlabeled　samples　in　the　target　domain.　The　prediction　results　with　high　confidence　are　selected　and　treated　as　pseudo　labels.　So　high-quality　pseudo　labels　can　be　gradually　generated.　Finally,　these　high-quality　pseudo　labels　are　used　to　train　the　model,　together　with　the　labeled　data　in　the　target　domain.　The　tongue　color　classification　model　can　be　obtained.　Considering　the　noisy　pseudo　labels,　the　contrast　regularization　function　is　adopted,　which　can　effectively　suppress　the　negative　impact　of　noisy　samples　in　the　training　process　and　improve　the　tongue　color　classification　accuracy　in　the　target　domain.　The　experimental　results　on　two　self-established　TCM　tongue　color　classification　datasets　show　that　the　classification　accuracy　of　tongue　color　in　the　target　domain　reaches　91.3%　when　only　20　labeled　samples　are　given　in　the　target　domain,　which　is　only　2.05%　lower　than　that　of　the　supervised　classification　model　in　the　target　domain.　©　2024　Science　Press.　All　rights　reserved.