Aiming at the problem that the data in the information fusion often overloads, the method that rough set application in neural network was proposed, in which useful attributes were extracted from given training data and redundant attributes were deleted utilizing numerical analysis ability of rough set theory, so sample size can be reduced. While reducing training time and increasing efficiency, the useful information in the source data set wasn't lost.