In fuzzy Cmeans (FCM) algorithm, the clustering result is sensitive to the initial center points and the clustering process does not take into account the influences of different density points. Thus, an improved FCM algorithm based on density peak and spatial neighborhood information is proposed. The improved algorithm selects the points with local density peaks or large local density values as the initial center points, and highlights high density points′ influence in the clustering. The theoretical analysis and experiments on both synthetic and realworld datasets from the UCI machine learning repository demonstrate that, the proposed algorithm has better antinoise, clustering performance and global convergence ability than traditional FCM algorithm.