Abstract: 【Purpose/significance】Aims to connect the fragmented answers in the social Q&A community and provide users with
high-quality answers on different topics and better knowledge services.【Method/process】First, we uses the Doc2vec algorithm to cal⁃culate the semantic similarity between answers and constructs the answer semantic network. Secondly, the Louvain algorithm is used to divide the answer semantic network, and the TextRank algorithm is used to extract the keywords of the documents under each topic,and the word cloud is used to visually display each topic. Finally, the PageRank algorithm is used to sort the answer semantic network after clustering, so as to realize the topic aggregation and sorting of answer documents.【Result/conclusion】Weconducted an empirical study using the question and answer data on“Zhihu”. The results show that the proposed answer aggregation and ranking method can not only visually show users the correlation strength between answers and the main content of each topic answer, but also provide users with topic-based answer ranking results, and automatically filter high-quality answers for users.【Innovation/limitation】An innovative answer semantic network is proposed, and based on the answer semantic network, we proposean answer knowledge organization meth⁃od integrating aggregation, topic visualization and ranking.