There　are　many　characteristics　of　tongue　that　need　to　be　analyzed　in　traditional　Chinese　medicine　(TCM).　Different　characteristics　are　often　analyzed　by　individual　methods,　which　significantly　increases　the　overall　implementation　complexity　of　the　analysis　system.　Therefore,　this　paper　proposes　a　collaborative　classification　method　of　tongue　color　and　coating　color　in　TCM　based　on　continual　learning.　This　method　takes　tongue　color　classification　as　an　old　task　and　coating　color　classification　as　a　new　task,　which　makes　full　use　of　the　similarity　and　relevance　of　the　two　tasks　to　realize　the　accurate　classification　of　tongue　color　and　coating　color　simultaneously　under　a　single　network　framework.　First,　a　dual　branch　network　structure　with　global　local　hybrid　attention　(GLHA)　mechanism　was　designed,　which　aggregates　high-level　semantic　features　with　low-level　features　to　improve　the　representative　capability　of　features.　Second,　a　continual　learning　strategy　based　on　the　combination　of　regularization　and　rehearsal　was　proposed,　which　made　the　network　effectively　prevent　forgetting　the　knowledge　learned　from　old　task　while　learning　new　task.　The　experimental　results　on　two　self-established　TCM　tongue　datasets　show　that,　the　proposed　collaborative　classification　method　can　achieve　a　comparable　classification　performance　with　a　single　task,　and　simultaneously,　reduce　the　overall　complexity　of　the　two　classification　tasks　by　almost　half.　Among　them,　the　accuracy　of　tongue　color　classification　reaches　93.　92%　and　92.　97%,　the　precision　reaches　93.　69%　and　92.　87%,　the　recall　reaches　93.　96%　and　93.　16%,　respectively.　While　that　of　the　coating　color　classification　reaches　90.　17%　and　90.　26%,　the　precision　reaches　90.　05%　and　90.　17%,the　recall　reaches　90.　24%　and　90.　29%,　respectively.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.