Opinion leaders in social networks play an important role in the process of information dissemination. The traditional mining of opinion leaders is based on network structures and doesnot consider the role of a specific topic or event， and the current mining of opinion leaders based on topic is only based on a single network structure， without taking into account the multiple interactive relationships between nodes. This paper proposes a topic opinion leader mining method based on multi-relational networks （MRTRank）， which joins topic factors and a variety of interactive relationship between nodes. Through an attribute network representation learning algorithm， the similarity of different nodes in the multi-relationship network is obtained， and the transition probability matrix of nodes is formed. Finally， the top-k opinion leaders are obtained through the PageRank algorithm. Experimental results on real Twitter datasets verify that the proposed method is superior to traditional opinion leader mining algorithms.