Abstract. The rapid development of the Internet brings the convenience but also results in "information trek" problem, which is solved by personalized recommendation algorithms. Traditional collaborative filtering algorithms face data sparsity problem. In order to solve those problems, this paper proposes a dynamic adaptive collaborative filtering recommendation algorithm based on user attributes. The algorithm first uses the item properties to build the user preference matrix, and then establishes the user rating preference matrix according to different behavior habits of users. Finally, based on these two matrixes, it prefills and dynamically adjusts the rating matrix. Experimental results show that the algorithm is more accurate in terms of the recommendation accuracy compared with traditional algorithms.