The data processing algorithm is studied to improve Wi-Fi fingerprint indoor positioning performance. Firstly, Wi-Fi fingerprint samples are collected and then are put into MySQL database and R project. Secondly, the Wi-Fi fingerprint data is divided into several clusters, and the K-mean clustering (K-Means) and fuzzy C-means clustering (FCM) are used to cluster the Wi-Fi fingerprint respectively. Finally, an enhanced clustering strategy (ECS) is proposed to for Wi-Fi fingerprint matching. Experimental results show that ECS reduces the positioning time-consuming about 50%-80% than that consumed by only using FCM and the positioning accuracy is also improved; ECS improves about 20%-40% than that obtained by only using K-Means in terms of positioning accuracy and it proves positioning stability and can automatically update the Wi-Fi fingerprint database.