Citation network is often used for academic recommendation. However, it is difficult to achieve high recommendation accuracy and low time complexity because it is often very large and sparse and differ-ent citations have different purposes. What’s more, some citations include unreasonable information, such as in case of intentional self-citation. To improve the accuracy of cita-tion network-based academic recommendation and reduce the time complexity, we propose an academic recommendation method for recommending authors and papers. In which, an author-paper bilayer citation network is built, then an enhanced topic model, Author Community Topic Time Model (ACTTM) is proposed to detect high quality author com-munities in the author layer, and a set of attri-butes are proposed to comprehensively depict the author/paper nodes in the bilayer citation network. Experimental results prove that the proposed ACTTM can detect high quality author communities and facilitate low time complexity, and the proposed academic rec-ommendation method can effectively improve the recommendation accuracy.