People spend 80% of the time for work and life in indoor, indoor positioning is the hard demand of smart cities and many smart city-related applications are inseparable from location services. At present, the main indoor positioning technology includes: Bluetooth, RFID, UWB, geomagnetic, etc., but due to cost, deployment convenience and other issues, limiting its application development. As city WIFI further extensive coverage, mobile phones are the usual terminal to receive Wi-Fi signals, therefore, Wi-Fi-based indoor positioning not only save a lot of equipment costs, but also quick links, wide application and huge advantages. This paper proposes a KNN localization algorithm based on fingerprint timing features (TS-KNN), which uses the fingerprint of the current moment to select the reference coordinates, and uses the positioning results of the first few moments for each reference coordinate. Perform weight correction. The experimental test results in a square in Chongqing show that the proposed TS-KNN method is superior to the KNN and WKNN algorithms, which can effectively improve the accuracy of indoor positioning and reduce the average positioning error.