For　Chinese　Named　Entity　Recognition　(NER)　tasks,　achieving　better　performance　with　fewer　training　samples　remains　a　challenge.　Previous　works　primarily　focus　on　enhancing　model　performance　in　NER　by　incorporating　additional　knowledge　to　construct　entity　features.　These　approaches　neglect　the　semantic　information　of　entity　labels　and　the　information　of　entity　boundaries.　Moreover,　conventional　methods　typically　treat　NER　as　a　sequence　labeling　task,　which　makes　them　inadequate　for　addressing　the　issue　of　nested　entities.　We　propose　a　new　span-based　approach　by　using　contrastive　learning　and　prompt　learning　to　address　these　problems.　By　pulling　similar　entities　closer　together,　pushing　dissimilar　entities　further　apart,　and　leveraging　entity　label　information,　we　improve　model　performance　in　few-shot　scenarios　effectively.　Experimental　results　demonstrate　that　our　method　achieves　significant　performance　improvements　on　a　sampled　Chinese　nested　medical　dataset　and　several　other　flattened　datasets,　providing　a　new　insight　into　addressing　challenges　in　few-shot　NER　tasks.