Text mining techniques have become popular in many disciplines in the era of big data. Author profiling analysis, a method of text mining, is useful for identifying the intellectual structures of research fields and the latest research topics that did not appear from author co-citation analysis. This study analyzed the changing intellectual structure of the Human Resources Development (HRD) field in Korea by investigating researcher groups, research areas, and relationships between researchers based on research topics through author profiling analysis. Using selected authors' articles published during periods 2010-2014 and 2015-2019, an index was extracted from titles, abstracts, and keywords of a total 1961 articles. 2670 indexes were derived through the index cleaning process structured by expert verification and expert cross-validation. The cosine similarity matrix between authors was calculated by applying weights to the index, followed by a clustering and network analysis using PNNC algorithm through WNET program. The results of this study advance discussion surrounding the existing HRD paradigm, definitions of HRD, areas of HRD, theoretical foundations of HRD, and previous studies that analyzed research trends by providing additional layers of granularity and an expanded framework of thought across each topic. The findings also suggested implications for academic and practical aspects and further research.