Analysis of ESG hotspots and evolutionary trends based on clustering algorithms

Using TF·IDF and Kleinberg algorithms, this paper constructs a knowledge map of Chinese ESG literature from 2018 to 2023, which is included in the core journals of China Academic Journal Network. The map visualizes the publication volume, publishing institutions and authors, keyword clustering, and mutations, analyzing the research hotspots and evolutionary trends in the field of ESG in China. Based on the analysis, the study concludes and proposes recommendations: the number of publications has been steadily increasing year by year, showing explosive growth. It suggests strengthening communication, cooperation, and resource sharing among research institutions and authors, as well as highlighting emerging high-interest topics such as ESG disclosure, sustainable development, digital economy, and green technology innovation. Furthermore, it recommends establishing global ESG standards and guidelines, enhancing information disclosure and transparency, promoting cross-sector and cross-industry collaboration, and driving technological innovation and digital transformation through the utilization of techniques such as artificial intelligence and big data analysis. This would reduce the costs associated with collecting and analyzing ESG data, improve data quality and accuracy, and thus promote the deepening of ESG practices and the realization of sustainable development.