[Objective] This paper aims to automatically identify the academic expertise of researchers, which improves the research project evaluation and talent assessment. [Methods] Firstly, we adopted the Iceberg Model to describe the academic expertise of researchers. The visible part of the “iceberg” reveals the researchers’ areas of expertise and specialization, which identify their core competencies and main research directions. The lower part of the “iceberg” indicates the “comparative advantages” of researchers’ expertise. Then, we used labels to represent researchers’ expertise and utilized machine learning techniques such as LDA and BERT to extract, cluster, and generate matrices of academic labels. Finally, we proposed the self-focus and the peer-relative indexes to identify the researchers’ main areas and relative position in the scientific community. [Results] Using a sample of 20 researchers, we generated 8,985 sets of label words and their weights and described researchers’ expertise at a fine-grained level. And then, the “Self-Focus Index” and the “Peer-relative Index” were calculated based on the domain-researcher matrix (40×20). We found the proposed method can accurately reflect researchers’ expertise in specific research areas and relative positions within the scientific community. [Limitations] Future work should consider incorporating the temporal factor to capture the temporal evolution characteristics of researchers’ academic expertise. [Conclusions] The advantages of the proposed method are twofold. Firstly, the iceberg model effectively explains what researchers do and how well they do it. The model provides a theoretical basis for label extraction, index design, and enhancing interpretability. Secondly, in addition to quantifiable comparative expertise index calculations, the method achieves fine-grained, precise, and dynamic talent expertise profiling.