In　recent　years,　with　the　exploration　of　pathological　mechanisms　and　treatments　of　Long　COVID,　there　has　been　a　dramatic　increase　in　related　scientific　publications.　Effective　extraction　of　key　information　from　these　texts　is　of　great　importance　for　public　health　and　research　progress.　In　the　Long　COVID　context,　Named　Entity　Recognition　(NER)　can　be　used　to　identify　disease　names　as　well　as　symptoms,　which　can　help　to　analyze　the　sequelae　caused　by　COVID-19　and　its　relationship　with　other　diseases.　Distinguished　from　molecular　biomedical　text　mining,　which　focuses　on　the　identification　of　entities　such　as　genes,　proteins,　and　chemistries　and　their　relationships,　Long　COVID　text　mining　faces　problems　such　as　the　lack　of　publicly　labeled　datasets　and　the　heavy　workload　of　manual　annotation.　Moreover,　due　to　the　strong　domain　characteristics　of　Long　COVID　relevant　named　entities,　models　and　methods　that　have　achieved　great　performance　in　the　generic　domain　will　have　significantly　degraded　named　entity　recognition　performance　on　this　domain.　Based　on　the　above　problems,　we　constructed　a　Long　COVID　literature　abstract　NER　dataset　(LNER)　and　proposed　a　Long　COVID　biomedical　literature　NER　model　Bert-BiLSTM-IDCNN-ATT-CRF　(BBIAC).　First,　the　BERT-BiLSTM-CRF　model　is　constructed　on　the　LNER　dataset.　Then,　the　inflated　convolutional　neural　network　(IDCNN)　is　added　between　the　BiLSTM　and　the　CRF　layers　to　obtain　the　local　features　in　the　text　sequences.　Finally,　feature　enhancement　is　performed　by　fusing　the　features　of　global　and　local　information　using　the　attention　mechanism.　The　experimental　results　show　that　the　method　proposed　in　this　paper　for　Long　COVID　literature　can　accurately　extract　the　characteristic　information　of　Long　COVID　symptoms　and　diseases,　and　has　better　performance　compared　to　other　baseline　models.