The invention discloses a text classification feature selection approach for importance weighing. The text classification feature selection approach comprises the following steps: first step, counting data information of each candidate feature appearing in each class, and specially considering the semantic representation degree of the candidate features for texts during counting, namely importance; second step, using the data information obtained in the first step, using a relevance statistical magnitude computational formula, and computing the distinguishing capacity of each candidate feature for each class; and third step, computing the distinguishing capacity of each candidate feature for all classes in a summarized manner, ranking all the candidate features according to the total distinguishing capacity of each candidate feature for all classes, and outputting a feature list obtained through ranking.