The　location　fingerprint　algorithm　is　the　main　method　to　study　the　indoor　positioning　technology,　and　the　online　matching　algorithm　is　one　of　the　main　factors　affecting　the　indoor　positioning　accuracy.　At　present,　the　matching　algorithms　in　online　stage　include　the　nearest　neighbor　algorithm,　K-nearest　neighbor　algorithm　and　weighted　K-nearest　neighbor　algorithm.　However,　these　three　algorithms　do　not　take　into　account　the　influence　of　the　fluctuation　of　AP　signal　on　the　positioning　result.　In　order　to　improve　the　matching　algorithm　in　online　stage,　a　weighted　K-nearest　neighbor　algorithm　based　on　the　improved　discrete　coefficient　is　proposed.　In　offline　stage　the　purpose　is　to　establish　a　fingerprint　database,　in　the　online　stage　using　discrete　coefficient　to　reflect　the　stability　of　the　various　AP　signal　and　treat　the　anchor　point　with　weighted　Euclidean　distance　between　the　reference　point,　calculate　all　the　weighted　Euclidean　distance,　choose　the　nearest　k　reference　points,　so　as　to　estimate　the　physical　location　of　pending　sites.　Finally,　experiments　show　that　the　weighted　K-nearest　neighbor　algorithm　based　on　the　improved　discrete　coefficient　can　achieve　an　average　positioning　accuracy　which　is　15%-17%　higher　than　the　K-nearest　neighbor　algorithm　and　11%-13%　higher　than　the　weighted　K-nearest　neighbor　algorithm.　©　2022　Beijing　University　of　Aeronautics　and　Astronautics　(BUAA).　All　rights　reserved.