Abstract: K-means clustering algorithm clusters datasets on the premise that the number of clusters is certain and initial clustering centers are selected randomly.In general the value of k cann’t be confirmed beforehand，and randomly selected initial clustering centers make the result of clustering unstable.A new method for determining optimal number of clusters in K-means clustering algorithm is presented to analyze the clustering quality and determine optimal number of clusters through making the number of clusters produced by AP be the upper limit kmax of search range for the number of clusters，selecting the Silhouette validity index and setting initial clustering centers based on maximum and minimum distance algorithm.Simulation experiment and analysis demonstrate the feasibility of the above-mentioned algorithm.