Abstract:　Named　entity　recognition　(NER)　is　an　important　task　in　natural　language　processing　(NLP).　In　recent　years,　NER　has　attracted　much　attention　in　the　biomedical　field.　However,　due　to　the　lack　of　biomedical　named　entity　identification　datasets,　the　complexity　and　rarity　of　biomedical　named　entities　and　so　on,　biomedical　NER　is　more　difficult　than　general　domain　NER.　So　in　this　paper,　we　propose　a　framework　(MMBERT)　based　on　Transformer　to　solve　the　problems　above.　To　address　the　scarcity　of　biomedical　named　entity　recognition　datasets,　we　introduce　ERNIE-Health,　a　new　Chinese　language　representation　model　pre-trained　on　large-scale　biomedical　text　corpora.　Because　of　the　complexity　and　rarity　of　biomedical　named　entities,　we　use　the　Bert　and　CW-LSTM　structures　to　get　the　joint　feature　vector　of　word　pairs　relations.　In　addition,　we　design　multi-granularity　2D　convolution　to　refine　the　relationship　and　representation　between　word　pairs.　Finally,　we　design　a　convolutional　neural　network　(CNN)　structure　and　a　co-predictor　to　improve　the　model’s　generalization　capability　and　prediction　accuracy.　We　have　conducted　extensive　experiments　on　three　benchmark　datasets,　and　the　experimental　results　show　that　our　model　achieves　the　best　results　compared　with　several　baseline　models　in　the　experiment.　Graphical　abstract:　[Figure　not　available:　see　fulltext.].　©　2023,　International　Federation　for　Medical　and　Biological　Engineering.