The invention discloses a weighted averaging personalized friend recommendation method based on LDA (Linear Discriminant Analysis). The method comprise the following contents: obtaining the first-degree, second-degree,..., m-degree friends of a target user t in a social network to form a candidate friend set C, and extracting the target user and the static attribute information of each user in theset C; utilizing an LDA topic modeling method, analyzing a topic to which the user pays attention so as to deduce the hobbies, the interests, the status and the age and gender information of the candidate user; analyzing the dynamic behavior information in the social network, and using a weighted average method to calculate a similarity among users; and finally, obtaining the grading vector of the target user for the candidate users, carrying out sorting according to the grading vector, and recommending the candidate users which rank in the Top-N to the target user. By use of the method, theattribute information of the target user and the candidate users can be more comprehensively considered so as to improve friend recommendation accuracy to a maximum degree. By use of the method, verification is carried out on a microblog dataset, and an experiment result indicates that the method has a good recommendation effect.