Although　significant　progress　has　been　made　in　Chinese　Named　Entity　Recognition　(NER)　methods　based　on　deep　learning,　their　performance　often　falls　short　in　few-shot　scenarios.　Feature　enhancement　is　considered　a　promising　approach　to　address　the　issue　of　Chinese　few-shot　NER.　However,　traditional　feature　fusion　methods　tend　to　lead　to　the　loss　of　important　information　and　the　integration　of　irrelevant　information.　Despite　the　benefits　of　incorporating　BERT　for　improving　entity　recognition,　its　performance　is　limited　when　training　data　is　insufficient.　To　tackle　these　challenges,　this　paper　proposes　a　Feature　Enhancement-based　approach　for　Chinese　Few-shot　NER　called　FE-CFNER.　FE-CFNER　designs　a　double　cross　neural　network　to　minimize　information　loss　through　the　interaction　of　feature　cross　twice.　Additionally,　adaptive　weights　and　a　top-k　mechanism　are　introduced　to　sparsify　attention　distributions,　enabling　the　model　to　prioritize　important　information　related　to　entities　while　excluding　irrelevant　information.　To　further　enhance　the　quality　of　BERT　embeddings,　FE-CFNER　employs　a　contrastive　template　for　contrastive　learning　pre-training　of　BERT,　enhancing　BERT＇s　semantic　understanding　capability.　We　evaluate　the　proposed　method　on　four　sampled　Chinese　NER　datasets:　Weibo,　Resume,　Taobao,　and　Youku.　Experimental　results　validate　the　effectiveness　and　superiority　of　FE-CFNER　in　Chinese　few-shot　NER　tasks.