Focusing on the issue of feature selection in text categorization, an interaction maximum feature selection algorithm, called Max-Interaction, was proposed. Firstly, an information theoretic feature selection model was established based on Joint Mutual Information (JMI). Secondly, the assumptions of the existing feature selection algorithms were relaxed, and the feature selection problem was transformed into an interaction optimization problem. Thirdly, the maximum of the minimum method was employed to avoid the overestimation of higher-order interaction. Finally, a text categorization feature selection algorithm based on sequential forward search and high-order interaction was proposed. In the comparison experiments, the average classification accuracy of Max-Interaction over Interaction Weight Feature Selection (IWFS) was improved by 5.5%; the average classification accuracy of Max-Interaction over Chi-square was improved by 6%; and Max-Interaction outperformed other methods on 93% of the experiments. Therefore, Max-Interaction can effectively improve the performance of feature selection in text categorization.