The traditional Wi-Fi indoor positioning methods need to match all fingerprint data in the fingerprint database before positioning， resulting in low positioning efficiency and poor experience in the crowd gathering area. Therefore， a multi-fingerprint database indoor positioning method based on Channel State Information （CSI）， K-means clustering algorithm and Support Vector Regression （SVR） algorithm was proposed. Firstly， according to the cluster distribution characteristics of CSI， K-means algorithm was used to cluster the CSI data in all positioning points to obtain the CSI data of multiple clusters. Then， multiple fingerprint databases were established based on multiple clusters， and the CSI data was stored in multiple fingerprint databases. After that， SVR models were trained in each fingerprint database for Wi-Fi positioning. Compared with the traditional Support Vector Machine （SVM） positioning method， the proposed method needs less training samples in the off-line stage， which improves the positioning efficiency； in the online stage， this method not only reduces the matching complexity， but also improves the positioning accuracy. Due to the use of multi-fingerprint database， the Wi-Fi positioning system can adjust the resource allocation strategy in real time according to the traffic， so as to improve the server operation efficiency and positioning service experience.