The　integration　of　lexicon　information　into　character-based　models　is　a　hot　topic　in　Chinese　Named　Entity　Recognition(NER)　research.　Most　methods　only　utilize　information　from　a　single　lexicon　which　is　usually　a　general　lexicon.　However,　In　the　Chinese　medical　text　scenario,　due　to　the　large　amount　of　medical　terminology,　a　single　lexicon,　especially　a　general　lexicon,　offers　little　performance　improvement　to　the　Chinese　NER.　In　this　paper,　we　propose　a　Multi-source　Lexicon　Information　Fusion　method　for　Named　Entity　Recognition　in　Chinese　Medical　Text(MLNER)　which　can　utilize　information　from　both　general　and　medical　lexicons.　Considering　the　small　medical　annotated　corpus,　we　combine　the　model　with　the　pre-trained　model　to　improve　the　performance　of　the　model　on　small　datasets　by　exploiting　the　rich　representation　capability　of　the　pre-trained　model.　Experiments　show　that　our　method　can　effectively　improve　the　performance　of　NER　in　Chinese　medical　text.　Our　model　is　also　applicable　to　Chinese　NER　tasks　in　other　domain　specific　fields,　with　good　scalability　and　application　value.