This　paper　introduced　the　semi-Markov　Conditional　Random　Fields　(semi-CRFs)　model　based　method　for　Chinese　Encyclopedia　text　topic　segmentation.　The　authors　adopted　HMM　model　state　posterior　as　the　basic　segmentation　clue　which　was　adjusted　to　each　text　instance　to　overcome　the　topic　duplication　problem　of　fully　connected　state　HMM　model　and　CRF　model.　The　authors　also　used　several　segment　level　word　semantic　features　derived　from　domain　thesaurus,　and　additional　topic　specific　clue　phrases　to　make　the　method　more　adapted　to　target　domain.　The　experiment　result　showed　that　this　method　was　suitable　for　Chinese　Encyclopedia　text　topic　structure　and　achieved　better　performance　than　HMM　model　and　CRF　model.