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In order to solve this problem, the research combines the Naive Bayes algorithm with the feature two-dimensional information gain weighting method, uses the feature weighting method to optimize the Naive Bayes algorithm, and calculates the dimension of different documents and data categories through a new feature operation method. The data gain between them can improve its classification performance, and the classification models are compared and analyzed in the actual Chinese and English databases. The research results show that the classification accuracy rates of the IGDC-DWNB model in the Sogou database, 20-newsgroup database, Fudan database and Ruster21578 database are 0.89, 0.89, 0.93, and 0.88, respectively, which are higher than other classification models in the same environment. 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