Named　Entity　Recognition　(NER)　is　a　fundamental　task　in　natural　language　processing.　Syntax　plays　a　significant　role　in　helping　to　recognize　the　boundaries　and　types　of　entities.　In　comparison　to　English,　Chinese　NER,　due　to　the　absence　of　explicit　delimiters,　often　faces　challenges　in　determining　entity　boundaries.　Similarly,　syntactic　parsing　results　can　also　lead　to　errors　caused　by　wrong　segmentation.　In　this　paper,　we　propose　the　dual-grained　syntax-aware　Transformer　network　to　mitigate　the　noise　from　single-grained　syntactic　parsing　results　by　incorporating　dual-grained　syntactic　information.　Specifically,　we　first　introduce　syntax-aware　Transformers　to　model　dual-grained　syntax-aware　features　and　a　contextual　Transformer　to　model　contextual　features.　We　then　design　a　triple　feature　aggregation　module　to　dynamically　fuse　these　features.　We　validate　the　effectiveness　of　our　approach　on　three　public　datasets.　©　2024　IEEE.