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Naive Bayes algorithm is a simple and efficient classification algorithm, but its conditional independence assumption is not always true in real life which is affected to some extent. Weighted Naive Bayesian classifier relax the conditional independence assumption to increase accuracy. Based on Identifiably matrix of Rough Set, a new weighted naive Bayes method based on attribute frequency is proposed. Different condition attributes are weighted differently; the Naive Bayesian classification algorithm performance is improved effectively. Experiments have proved that the calculation of this algorithm is easier and more effective.