Medical　Named　Entity　Recognition　is　essential　for　structuring　medical　text　data,　thereby　aiding　in　the　creation　of　medical　applications　like　knowledge　graphs　and　diagnostic　systems.　Contemporary　methods　predominantly　utilize　models　of　word　embedding,　subsequently　augmented　by　various　models　for　semantic　comprehension,　to　enhance　entity　recognition　performance.　However,　in　the　medical　domain,　specialized　terminologies　pose　a　challenge　for　general　domain　word　embedding　models.　Furthermore,　current　methods　frequently　neglect　local　semantic　attributes　and　face　challenges　in　fully　grasping　global　semantic　features,　attributed　to　the　dimensional　constraints　of　word　embeddings.　To　address　these　challenges,　we　propose　the　Stacked　Attention　Network　(SAN)　for　Chinese　medical　NER.　We　fine-tune　RoBERTa　using　real-world　electronic　medical　record　data　to　incorporate　medical　term　features　and　utilize　a　CNN　model　to　extract　local　semantic　features.　Furthermore,　we　introduce　a　stacked　BiLSTM　with　a　multi-layer　structure　to　effectively　capture　global　semantic　information.　Experimental　results　on　real-world　medical　text　data　demonstrate　that　our　SAN　model　achieves　an　F1　score　of　91.5%,　outperforming　other　advanced　NER　models.　　©　2024　IEEE.