近年来，社区问答服务系统（ CQA ）越来越受到人们的欢迎，但随着提问规模的膨胀，获得回答的问题比重逐步降低，且答案质量无法得到保障 . 为了提高问答系统中问题被解答的概率，并提升答案可信度，文中提出了基于社交关系相似度的社交问答系统（ SQA ），主动寻找与提问者社交关系紧密且能够回答问题的用户，并提出了针对提问者与最佳回答者的推荐方法 . 实验结果表明，在主观性强或实时性强等问题集上，文中方法能更快地得到让提问者满意的答案.

In recent years, community question answering system (CQA) are becoming popular. But with the expansion of the scale of the questions, the proportion of questions that got answered gradually reduced, and the quality of the answers cannot be guaranteed. In order to increase the answering probability of the questions in Q&A system, and enhance the credibility of answer, we put forward Social question answering System based on the social relationship similarity measure. Then we raise a method to find suitable respondents who are willing to answer and are familiar with related field. Experimental results show that the method of this paper can get satisfactory answers faster compared with the traditional Q&A System on the subjectivity or real-time problem sets.