The invention relates to a text feature selection method based on unbalanced data sets. Feature sets of unbalanced documents are calculated on a computer; and modelling is carried out by selecting a classification algorithm model. The text feature selection method specifically comprises the following steps of: (1), dividing the data sets into majority classes and minority classes, stipulating the minority classes as positive classes represented by ci, and stipulating the majority classes as negative classes represented by a formula shown in the specification; (2), pre-processing texts in the data sets, and executing operations, such as word segmentation and removing of stop words, so as to form a set T of features t; (3), respectively calculating parameters A, B, C, D and N corresponding to various features t in the unbalanced class documents; (4), respectively calculating new X2(t,ci) of various features t under different classes in the unbalanced class documents; (5), respectively setting threshold values for screening features in the unbalanced class documents, according to the X2(t,ci) calculated by various features, arranging according to the size order; and taking out a feature set T' including an appointed number of features according to the classes; and (6), selecting a proper classification algorithm model (such as a decision tree, a support vector machine and Bayes) to model according to the feature set T' after the features are selected.