Prompt-based　pre-trained　language　models　(PLMs)　have　demonstrated　their　superior　performance　on　a　wide　variety　of　downstream　tasks.　In　particular,　the　performance　of　prompt　tuning　has　significantly　outperformed　traditional　fine-tuning　in　the　zero-shot　learning　and　few-shot　learning　scenarios.　The　core　idea　of　prompt-tuning　is　to　convert　different　downstream　tasks　to　mask　language　modeling　problems　through　prompts,　which　can　bridge　the　gap　between　pre-training　tasks　and　downstream　tasks　for　better　results.　Verbalizer,　as　an　important　part　of　prompt-tuning,　can　largely　determine　the　final　performance　of　the　model,　but　the　design　of　the　Chinese-based　verbalizer　is　yet　to　be　fully　explored.　In　this　paper,　we　propose　a　method　to　expand　the　verbalizer　by　extracting　knowledge　from　the　training　set　based　on　a　Chinese　text　classification　task.　In　brief,　we　first　segment　the　Chinese　training　set,　then　filter　the　words　that　can　express　the　semantics　of　the　labels　by　semantic　similarity,　and　finally　add　them　to　the　verbalizer.　Extensive　experimental　results　on　multiple　text　classification　datasets　show　that　our　approach　significantly　outperforms　ordinary　prompt-tuning　and　outperforms　other　methods　for　constructing　the　verbalizer.　©　2023　SPIE.