Traditional rough set-based approaches to reduct have difficulties in constructing optimal decision tree, such as empty branches and over-fitting, selected attribute with more values, and increased expense of computational effort. It is necessary to investigate fast and effective search algorithms. In this paper, to address this issue, the limitations of current knowledge reduction for evaluating decision ability are analyzed deeply. A new uncertainty measure, called decision degree, is introduced. Then, the attribute selection standard of classical heuristic algorithm is modified, and the new improved significance measure of attribute is proposed. A heuristic algorithm for rule extraction from decision tree is designed. The advantages of this method for rule extraction are that it needn’t compute relative attribute reduction of decision tables, the computation is direct and efficient, and the time complexity is much lower than that of some existing algorithms. Finally, the experiment and comparison show that the algorithm provides more precise and simplified decision rules. So, the work of this paper will be very helpful for enlarging the application areas of rough set theory.