High-precision indoor positioning has broad market prospect. In traditional indoor positioning algorithm based on WKNN, it is difficult to deal with a target space of large area, and its position estimation results face the matters of inaccurate and instability as rebounding or clustering. To solve these problems, this study proposes a WKNN indoor positioning algorithm based on spatial characteristics partition and former location restriction. According to the proposed algorithm, target space of large area is divided into multiple partitions by its spatial characteristics, which solved the problem that one fingerprint database cannot achieve total coverage. It also introduced the restricted relationship between the former and the present position, which improved the quality of candidate reference points and thus improved the smoothness of the estimation results. Results of a large number of indoor positioning experiments in real environment show that the proposed algorithm can effectively improve the indoor positioning accuracy when compared with the traditional WKNN.