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The traditional indoor location algorithm based on distance-loss model mostly turn received signal strength indicator RSSI into distance, and then through the location-distance algorithm to achieve positioning. These algorithms need fit the wireless signal propagation model parameters A and N through experience or large amounts of data, so they are dependent on experience and are not strong universal algorithms for location of the different environment, also low accuracy. After lots of research and analysis of radio signal propagation model and the traditional indoor location algorithm, a new indoor location algorithm uses BP neural network to fit the distance-loss model is proposed. From a number of distances between reference nodes and blind node, Taylor series expansion algorithm is used to determine the coordinates of the blind node. Finally, the experiment result shows that the new algorithm improves the positioning accuracy and universality, compared with the traditional positioning algorithms.