Association categorization approach based on frequent patterns has been recently presented, which builds the classification rules according to frequent patterns in various categories and classifies the new text employing these rules. But there are two shortages when the method is applied to classify text data: one is that the method ignores the information about word’s frequency in a text; another is that the rule pruning to improve the classification efficiency will lead to obvious descending of accuracy when mass rules are generated. Therefore, a text categorization algorithm based on frequent patterns with term frequency is presented. This study illuminates that the word frequency is helpful for improving the accuracy of the association categorization and the classification rule tree can improve the efficiency of the association classification. The result of experiments shows the performance of association classification is better than three typical text classification methods Bayes, kNN (k nearest neighbor) and SVM (support vector machines), so it is a promising text classification method.