Abstract: The paper puts forward the way which includes the rough set theory and the cluster of attribute’s values by optimizing decision tree from three principles.First,the relative core and relative reduction based on information entropy is worked out by rough set theory,which decreasing the decision tree’s nodes in number and the decision tree’s path in depth.Second,when the characteristic attributes that the information entropy is most gained are selected,by clustering attribute’s values,the curve shows the peak of distribution,or approximately.By analyzing the data of UCI database,the results show that the algorithm greatly decreases decision tree’s nodes in number and the depth of the paths.