To promote positive values and foster a good social atmosphere, all walks of life launch some benchmark selection activities, such as “the most beautiful doctor” and “the most beautiful teacher”. These role models (or benchmarks) tend to be a minority among numerous candidates. However, traditional methods have difficulty evaluating tens of thousands of candidates and do not consider the learning relationships and specific advantages among candidates. Thus, it is difficult to realize benchmark identification by traditional methods. To reduce the workload of benchmark identification, this study proposes a social network data envelopment analysis method for identifying benchmarks from a large-scale sample, by combining the data envelopment analysis (DEA) with social network analysis (SNA). First, a pairwise evaluation process is constructed using the DEA method to explore the efficiency state of each decision-making unit (DMU) relative to another DMU, and a pairwise evaluation matrix that reflects the reference relationship between any two DMUs is obtained. Then, a social network is built based on this matrix, and role-model DMUs in a large sample are identified by comparing the in-degree centrality values of all DMUs. Next, the specific advantages of the selected benchmarks are analyzed based on the interpretation of weights in the DEA model. Finally, an experiment is carried out using online diagnosis data from 10 418 doctors across 55 departments on The Chunyu Doctor Platform to verify the social network data envelopment analysis method.