The　Flat-LAttice　Transformer　(FLAT)　has　achieved　notable　success　in　Chinese　named　entity　recognition　(NER)　by　integrating　lexical　information　into　the　widely-used　Transformer　encoder.　FLAT　enhances　each　sentence　by　constructing　a　flat　lattice,　a　token　sequence　with　characters　and　matched　lexicon　words,　and　calculating　self-attention　among　tokens.　However,　FLAT　faces　a　quadruple　complexity　challenge,　especially　with　lengthy　sentences　containing　numerous　matched　words,　significantly　increasing　memory　and　computational　costs.　To　alleviate　this　issue,　we　propose　a　novel　lightweight　lexicon-enhanced　Transformer　(LLET)　for　Chinese　NER.　Specifically,　we　introduce　two　distinct　variants　that　focus　on　character　attention　to　characters　and　words,　both　jointly　and　separately.　Experimental　results　conducted　on　four　public　Chinese　NER　datasets　show　that　both　variants　achieve　significant　memory　savings　while　maintaining　comparable　performance　when　compared　to　FLAT.　©　2024　IEEE.