Rochester model-naive Bayesian model-based data classification system

The invention relates to a Rochester model-naive Bayesian model-based data classification system, which comprises a data processing module, a sampling module, a modeling module and a data testing module, wherein the data processing module divides an original sample set into a saturated layer and a lacking layer according to the input missing value ratio of each sample variable in the original sample set and relativity among the sample variables and sample attributes; the sampling module randomly extracts a training sample variable and a testing sample variable from the saturated layer and the lacking layer to form a training sample set and a testing sample set of which each comprises the saturated layer and the lacking layer respectively; the modeling module models training samples in the saturated layer through a Rochester regression model and models the training samples in the lacking layer through a naive Bayesian model to obtain a hybrid dynamic model with the Rochester regression model and the naive Baye