Proposes a new domain-knowledge independent algorithm of discretization of consecutive attributes based on decisions to amend the limitation that Rough Sets can only deal with the discrete attributes in data sets. Unlike traditional methods, the candidate breakpoint set is obtained after the calculation and sorting of the attribute significance of each consecutive attribute thus leads to a smaller set size and less computational complexity. At the same time, proposes some rules of reducing candidate breakpoints in order to increase the velocity of system convergence. Using the algorithm, the decision table after discretization will be always consistent and can reserve useful information as much as possible. Finally, the algorithm is knowledge-independent and can be used in different fields without any additional information.