The invention provides a text classification feature selecting method capable of reducing the characteristic dimension and the classification complexity and improving the classification accuracy. The method comprises the following steps that a feature set S and a target class C are obtained, the relevancy Rc(x(i)) between each feature x(i) in the feature set S and the target class C is calculated, and sort descending is conducted on the feature set S according to the size of the relevancy Rc(x(i)); the redundancy Rx and the synergy degree Sx between every two features in the feature set S are calculated, and the sensitivity Sen of each feature is calculated with the combination of the relevancy Rc(x(i)) between each feature and the target class, the sensitivities Sen are compared with a preset threshold th, and the feature set S is divided into a candidate set Ssel and an excluding set Sexc with the combination of the sort descending result of the feature set S according to the threshold th; the sensitivities Sen between the features in the candidate set Ssel and the features in the excluding set Sexc are calculated, the sensitivities Sen are compared with the preset threshold th, and the candidate set Ssel and the excluding set Sexc are adjusted according to the threshold th. The text classification feature selecting method is suitable for the field of machine learning text classification.