Background:　As　an　important　part　of　the　tongue,　the　tongue　coating　is　closely　associated　with　different　disorders　and　has　major　diagnostic　benefits.　This　study　aims　to　construct　a　neural　network　model　that　can　perform　complex　tongue　coating　segmentation.　This　addresses　the　issue　of　tongue　coating　segmentation　in　intelligent　tongue　diagnosis　automation.　Method:　This　work　proposes　an　improved　TransUNet　to　segment　the　tongue　coating.　We　introduced　a　transformer　as　a　self-attention　mechanism　to　capture　the　semantic　information　in　the　high-level　features　of　the　encoder.　At　the　same　time,　the　subtraction　feature　pyramid　(SFP)　and　visual　regional　enhancer　(VRE)　were　constructed　to　minimize　the　redundant　information　transmitted　by　skip　connections　and　improve　the　spatial　detail　information　in　the　low-level　features　of　the　encoder.　Results:　Comparative　and　ablation　experimental　findings　indicate　that　our　model　has　an　accuracy　of　96.36%,　a　precision　of　96.26%,　a　dice　of　96.76%,　a　recall　of　97.43%,　and　an　IoU　of　93.81%.　Unlike　the　reference　model,　our　model　achieves　the　best　segmentation　effect.　Conclusion:　The　improved　TransUNet　proposed　here　can　achieve　precise　segmentation　of　complex　tongue　images.　This　provides　an　effective　technique　for　the　automatic　extraction　in　images　of　the　tongue　coating,　contributing　to　the　automation　and　accuracy　of　tongue　diagnosis.