The invention provides a text classification feature selecting method based on term frequency and chi-square statistics. The method comprises the following steps of preprocessing a data set; transforming each data object into a vector of a feature space; respectively calculating the maximum term frequency and the average term frequency of each term in each text classification; calculating the distribution diversity of the term in each classification according to the average term frequency; combining the maximum term frequency and the distribution diversity of each term with a traditional chi-square statistical formula to calculate the weight of each term in each classification, and regarding the maximum value as the term weight; sorting the term weights according to a descending order, and selecting the former N terms with the maximum weight values to be as the features. By the method provided by the invention, the traditional chi-square statistics is improved, so that the contribution degree of certain feature items which have relatively high frequency of occurrence or are uniformly distributed in the classification is reflected, and the shortage that the traditional chi-square statistical method tends to low-frequency words is overcome.