Biomedical　named　entity　recognition　(BNER)　is　a　basic　task　of　the　extraction　of　medical　information.　The　existing　deep　learning-based　approaches　usually　represent　the　medical　text　by　using　words　or　characters.　However,　most　of　biomedical　terms　consist　of　many　words　(characters).　Splitting　them　into　many　fragments　(words　or　characters)　while　leveraging　the　attention　mechanism　to　assign　attention　scores　for　each　fragment　maybe　disperse　the　importance　weight　and　cause　a　lower　attention　score　for　the　biomedical　terms.　Therefore,　this　paper　presents　a　dictionary-based　adaptive　attention　network　for　BNER.　Specifically,　a　biomedical　dictionary　is　firstly　constructed　by　integrating　multiple　existing　medical　resources.　Secondly,　building　the　guidance　vectors　by　matching　the　electronic　medical　record　(EMR)　text　to　the　constructed　dictionary.　Then,　an　adaptive　attention　strategy　is　presented　to　guide　the　attention　mechanism　to　assign　higher　attention　to　the　overall　medical　term　by　using　the　guidance　vectors.　We　conduct　extensive　experiments　on　a　real-world　dataset,　the　results　illuminate　that　our　presented　method　outperforms　all　baselines.　©　2023　IEEE.