In　order　to　achieve　high　precision　on　indoor　location,　a　Wi-Fi　indoor　location　method　based　on　improved　back　propagation　(BP)　neural　network　is　proposed.　The　classical　BP　neural　network　is　optimized　in　real　time　by　the　ant　colony　optimization　algorithm.　Meanwhile,　the　momentum　term　is　introduced　to　construct　an　improved　four-layer　BP　neural　network　model.　The　model　uses　the　Wi-Fi　signal　feature　as　the　input　of　the　BP　neural　network　and　succeeds　in　the　area　classification　under　multiple　Wi-Fi　signal　features.　The　experimental　results　demonstrate　that　the　improved　BP　neural　network　can　increase　the　classification　accuracy　of　the　classifier　effectively,　and　achieve　a　high-precision　indoor　area　location.　Furthermore,　it　performs　better　practical　results　while　ensuring　the　time　complexity.　The　advantages　of　this　method　are　high　practicability,　low　cost,　high　prediction　classification　accuracy,　and　robust　stability,　which　can　achieve　the　efficient　classification　of　the　short-range　area.　©　TÜBTAK