Naive Bayes algorithm is based on feature-independence assumption and the traditional TF-IDF weighting algorithm, and only considers the distribution of features in the whole training set, but ignores the relationship between feature and categories or documents, so the weights given by traditional method cannot represent its performance. To solve the above problems, this study proposes a naive Bayes classification algorithm of feature weighting based on two-dimensional information gain. It considers the effects of two-dimensional information gain of features, which are the information gain of category and the information gain of documents. Compared with the traditional naive Bayesian algorithm of feature weighting, the proposed algorithm can improve about 6% in the precision, recall, F1 value performance.