Internet technology is developing rapidly. The developer community's question-answering based experience communication method has become one of the important means for many developers to solve problems encountered in software development and maintenance. How to promptly and accurately recommend a question responder to a questioner in the developer community is an important issue with practical needs. Through the collection and analysis of the data of two representative mainstream developers in Stack Overflow and Github, three phenomena are observed that affect the timeliness and accuracy of the above recommended questions:(1) User label customization phenomenon. In the developer community, the user's tag information is subjectively defined by the user, rather than the system is objectively calibrated according to the user's historical behavior; (2) Asymmetric activity. The user may be active in one or some developer communities, however, it is not equally active or even inactive in other communities; (3) Keyword set closure phenomenon. That is the question answerer in the developer community recommends only based on the keywords in the question text, but does not consider other semantic related key words. In view of the above problems, the user information of the developer community is integrated, the interaction between users and users is analyzed, a cross-community developer network is established, and an algorithm based on restart random walk is proposed to update user tags. Further, by using Taxonomy to expand the query keyword range of the problem, on the basis of this, the user matrix is more accurately recommended, and the range of effective users at the time of recommendation is increased. Finally, the experimental results of F-measure and NDCG are good, which can effectively improve the efficiency and accuracy of problem recommendation.