For identifying the product failure rate grade with diverse configuration and different operation condition,introduced the useful Bayesian networks classifiers.Also described their algorithms and characters in detail.On the basis of these classifier models,listed the procedure of building product failure rate grade classifier for guiding the modeling and application the actual cases.Carried out the France enterprise case study and the results show that,with the comparison to other Bayesian networks classifiers and traditional decision tree C4.5,the tree augmented nave-Bayes classifier get the best general performance with highest precision,which can build a firm keystone for later maintenance resource distribution and operation optimization.