Audience targeting which is designed to discover the prospective target users by analyzing the these seed users’ behavior is an important technology in the online advertising recommendation systems, and the existing audience targeting technologies mostly rely on collaborative filtering algorithms. However, the traditional collaborative filtering algorithms have the disadvantages of lower precision and weaker anti-attack capability. In order to solve the problems, an audience targeting algorithm based on neighbor choosing strategy is proposed. Firstly, the users which have the similar behavior with the seed audiences are chosen dynamically by means of the user behavior similarity. Then, on the basis of the users’ feature and behavior, the neighbors of each seed user are chosen from the behavior similar audiences by the user similarity, and all the neighbors are considered to be the candidate audiences. Finally, the prospective audiences are chosen from the candidate users by the audience targeting algorithm based on neighbor choosing strategy, so as to complete the task of audience targeting. Compared with the existing methods, the experimental results on real-world advertisement datasets show that the audience targeting algorithm not only improves the precision, but enhances the anti-attack capability as well.