Community question answering aims to connect queries and answers based on users' community behaviors, find the most relevant solutions for newly raised questions, and improve community activity. Existing research mainly focuses on the single-hop method, which relies on a particular feature for simple question answering. However, multi-hop question answering, which depends on multi-step reasoning to solve complex questions, still faces the destitute adaptability caused by various entity features and complex entity correlations. In this work, we put forward a Multi-Hop Community Question-Answering method called MHCQA, that combines the multi-aspect features of community entities, constrains the answer retrieval process from different perspectives and improves the adaptability of multi-hop answer retrieval. Firstly, we utilize phrases for representing the semantic features of question-answering and fuse them with entity constitution properties through heterogeneous graphs to effectively indicate the diverse relationships among entities in communities. At the same time, we bring a dynamic heterogeneous network construction strategy to improve the efficiency of feature representation. Secondly, we enhance heterogeneous graph attention networks for efficiently computing local entity similarities to find relevant answers to questions based on partial graphs. Finally, we propose a multi-constraint multi-hop community question-answering method, which optimizes answer retrieval from three aspects: the hop count, the number of answers, and the relevance of responses, to improve the scene adaptability of multi-hop answer retrieving. Abundant experiments on realistic corpus (Quora, Math, Super User, and Stack Overflow) illustrate that MHCQA improves the multi-hop question answering error rate by about 2 % and is about 11 % ahead of the baselines in terms of operating efficiency. Given our present literature survey, MHCQA is a promising research idea for automatic answering services in online question-answering communities.