In　order　to　reduce　the　influence　of　the　complex　time-varying　characteristic　of　received　signal　strength　indication　on　positioning　accuracy　in　indoor　wireless　local　area　network　(WLAN)　environment,　a　new　indoor　positioning　algorithm　based　on　linear　discriminant　analysis　(LDA)　and　gradient　boosting　decision　tree　(GBDT)　is　proposed　in　this　paper.　The　algorithm　adopts　LDA　to　extract　the　main　positioning　features　of　original　location　fingerprints　and　remove　the　redundant　localization　features　and　noise.　Then,　using　the　forward　distribution　algorithm,　the　negative　gradient　value　of　the　loss　function　in　current　model　is　taken　as　the　approximation　of　the　error　to　fit　a　classification　and　regression　tree.　The　additive　model　is　used　to　linearly　combine　the　resulting　classification　and　regression　trees　and　generate　a　GBDT　positioning　model.　The　experiment　results　show　that　compared　with　other　indoor　positioning　algorithms,　the　positioning　accuracy　of　the　proposed　algorithm　is　improved　by　more　than　20%,　and　the　algorithm　also　reduces　the　number　of　the　required　access　points.　©　2018,　Science　Press.　All　right　reserved.