A Data‐Driven Review of Geoscience Research Evolution Using <scp>SciBERT</scp> ‐Based Topic and Network Analysis

ABSTRACT This review provides a data‐driven synthesis of the evolution of geoscience research from 2004 to 2023. A field‐level bibliometric overview of 439,680 records indexed in the Web of Science Core Collection (Geosciences, Multidisciplinary) characterises broad publication patterns, while the in‐depth structural and semantic analysis focuses on the 200 most‐cited papers per year (4000 articles in total), to which keyword co‐occurrence network analysis and SciBERT‐based BERTopic modelling were applied. This two‐tier design situates an analysis of the field's most influential literature within its full publication landscape, enabling systematic examination of both the structural and semantic dynamics of geoscience. The keyword‐network analysis indicates that model, climate and water have consistently constituted the structural backbone of geoscience research, whereas the centrality of evolution, initially a dominant hub, declined steadily over the study period, while data‐ and technology‐oriented themes, such as remote sensing, GIS and machine learning, have gained increasing prominence since the 2010s. Complementary semantic patterns identified through the SciBERT‐based topic model reveal a parallel progression: early research emphasised geological and geotechnical processes; the 2010s were characterised by expanding attention to climate change, paleoenvironmental reconstruction and ecosystem dynamics; and the late 2010s onward show a growing focus on disaster response, resource management and advanced technological applications. In the most recent period, AI‐ and data‐intensive approaches emerge as a dominant thematic direction. Together, this review identifies three major paradigm transitions in geoscience research—from geology‐centred foundations, through climate‐ and environment‐oriented expansion, to AI‐driven and data‐intensive Earth science. Trend and change‐point analysis of annual topic prevalence in a single 2004–2023 topic model dates the statistically significant shift towards AI‐ and data‐intensive research to 2013–2014, whereas the climate‐ and environment‐oriented expansion proceeded gradually, without an abrupt transition. By offering a systematic and reproducible synthesis of long‐term disciplinary change, this review complements conventional narrative reviews and provides a reference framework for future cross‐domain research in the era of foundation models and scientific large language model applications.