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To deal with the problems that traditional K-means clustering algorithm is very dependent on the selection of the initial points, being prone to clustering result of local optimum rather than global optimum, and it is difficult to meet the need of dealing with massive amounts of data, an improved K-means clustering algorithm based on MapReduce is proposed. The algorithm combines systematic sampling method to get a representative sample set which is used to replace the massive data set; and uses density method and Max-Min distance method to get the optimal initial clustering centers; and adopts Canopy algorithm to get a rough clustering which can reduce the computational scale; and finally employs the idea of sequential composition of MapReduce programming model to realize the parallel extension of the algorithm, which can make full use of the computing and storage capacity of the cluster, in order to adapt to the application of massive data. The improved algorithm is compared with the traditional clustering algorithms in this paper, and the comparative results show that the performance of improved algorithm is better than the latter. The experiments show that the improved method reduces the dependence on the initial cluster centers and also reduces the number of iterations of clustering and the clustering time.Furthermore it shows greater performance advantage in dealing with massive data.