For indoor environment， the WiFi signal strength is susceptible to external interference. Due to its instability， the accuracy of matching in the fingerprint database is low and the positioning accuracy is not high. An optimization algorithm based on indoor fingerprint positioning is proposed. This algorithm optimizes the fingerprint database and matching algorithm. Database optimization uses limiting and moving average filtering for pre-processing. According to the indoor environment， assign the ID of the area to which the sampling point belongs to build a multidimensional fingerprint database. The matching algorithm is optimized to classify the points to be located according to the support vector machine （SVM） and obtain the corresponding area ids. The Euclidean distance， Manhattan distance and Chebyshev distance are combined to obtain a position estimate. Finally， combined with the pedestrian dead reckoning （PDR） algorithm， the obtained step size and heading angle are subjected to particle filtering to achieve positioning. The proposed algorithm improves the positioning accuracy by 13.92%.