Detecting user communities with denser common interests and network structure plays an important role in target marketing and self-oriented services. User-Generated content and the relationship between the users are often separated in the current methods on community detection, which results in the unreasonable community structures. Though some methods tried to combine the two factors, they are complex. Link community algorithm (LCA) is an efficient state-of-art method on overlapping community discovery. However, LCA does not take into account the real interest characteristics when calculating the similarity between the links. To solve the issues on user community detection on Micro-blog, this paper proposes a R-C model which takes the user relationships as the network nodes, treats the intersection of the interest characteristics of the two users in a link as the link's interest characteristics, and makes the shared user between two links as the underlying link between the links. Also, the community detection method based on the R-C model is discussed, and the complexity in clustering is analyzed. Finally, compared with node CNM and LCA, the method using R-C model is proved to be better in finding closer relationship and denser common interest user communities.