In an open academic environment, academic exchanges and scientific research cooperation are pivotal to academic innovation and development, and helping researchers find suitable academic groups contributes greatly to their inspiration. Most of the existing approaches to community detection of researchers focus on the correlation between research results and ignore that between their own academic activities. As such, this study uses Open Research and Contributor ID (ORCID) data to build a network of academic information by analyzing researchers’ academic activities. The cross-layer edge clustering coefficient is improved on the basis of similarity between nodes at all levels, and then a model detecting the researcher community based on the weighted cross-layer edge clustering coefficient is proposed. The model extracts the direct correlations between researchers by constructing multiple meta-paths and stratifies the network according to different attribute relationships. Inter-node similarity is calculated with weighted cross-layer edge clustering coefficients. Then the network is transformed into a homogeneous network which is combined with the Louvain algorithm for community detection. Experiments are carried out in both artificial and real networks, and the results are evaluated according to the actual situation of the community, improving the division while avoiding the uncertainty of parameters.