Nai¨ve Bayesian is a simple and efficient pattern recognition algorithm, and has been widely used in text classification. But the assumption of Nai¨ve Bayesian is often not hold in the real application. To improve the performance of the Classifier, a weighted Bayesian method is proposed based on feature selection weight for taking into account different conditions have different effects to the decision conditions. Firstly, represent the effect value of every feature by the combination of the Chi Square value and IDF (Inverse Document Frequency). Then, the weight of every feature is computed by the effect value. Lastly, weighted Bayesian Classifier is built on the weight. By experiments, This method has a better classification performance than Nai¨ve Bayesian Classifier.