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The traditional indoor wireless location algorithm based on distance-loss model mostly need fit the parameters A and n of the wireless signal propagation model through experience or large amounts of experiment data, so they do not fully reflect the real volatile environment, also result in low accuracy. After lots of research and analysis of radio signal propagation model and the traditional indoor location algorithm, a new indoor location algorithm using BP neural network to fit the distance-loss model is proposed. From a number of distances between reference nodes and blind node, a more accurate six-point centroid algorithm is used to estimate the position of the blind node instead of using the traditional three-point centroid algorithm. Finally, the experiment result shows that the new algorithm improves the positioning accuracy and universality, compared with the traditional positioning algorithms.