The invention discloses a method for a personalized recommendation of a Web text. The method comprises the following steps: performing feature extraction on a plurality of kinds of Web texts generated before a certain time t so as to obtain a feature matrix E of a Web text set, and then performing cluster so as to obtain n categories; besides, according to a time span hj from the time when a Web text oj in a Web text subset relevant to the behavior of a certain user ui before the time t to the time t, calculating out a preference influence degree dj to the user ui so as to obtain a pair of category number-influence degree cj of the Web text oj and generate a dynamic preference vector of the user ui; if the preference influence degree of the user and the category of the Web text to be recommended is found to be higher than or equal to a threshold value tan, recommending the Web text to be recommended to the user. According to the method disclosed by the invention, the dynamic influence changed with the time lapse of current preference from the historic behavior of the user is considered, and the method is more accurate in recommendation, has dynamic performance, and more conforms to the actual condition.