The rapid increase in the number of community-based question-and-answer services is attracting many users. Questions are posted and answered by community members. These users, who can help other users answer questions, can be considered experts. To facilitate finding a suitable expert and alleviate information overload, in this paper, expert yellow pages (EYP) for community question answering (CQA) are constructed. Considering the various lengths of texts, the biterm topic model (BTM) is used to model questions and fields of expertise. Then, two-dimensional EYP (2DEYP), which are composed of expertise field dimensions and question dimensions, are constructed. The intersections represent the cluster of experts. The proposed 2DEYP can be expanded both laterally and vertically for a more in-depth understanding and a more precise location. As the closer neurons represent similar topics, a novel labelling method is proposed to identify topic words for navigation. The method uses the distance between neurons as the differentiation capability. To further distinguish experts, a ranking mechanism is proposed. The experts can be ranked by integrating their expertise and activity levels. The expertise level is novel and characterized by both breadth and depth aspects. The proposed approach is evaluated via a real dataset, and the experimental results show that the proposed algorithm is feasible and performs well.artificial neural network, community question answering, expert yellows pages, knowledge management 1 | INTRODUCTION Community question answering (CQA) websites, such as Yahoo! Answers 1 and Zhihu, 2 are typical applications of Web 2.0 technology focusing on knowledge sharing. Community members seeking help post questions, and others share their knowledge by answering these questions (Yang et al., 2019). Because of their convenience, CQA websites attract many users. For example, Zhihu had more than 220 million users in November 2018. 3 Most of these users were expert users who could answer questions. The definition of an expert in CQA is much broader in that each member may have a higher level of expertise in a certain knowledge area (Liu,