ABSTRACT Place names—functioning as both locational identifiers and symbolic texts that encode power, economy, culture, and collective memory—exhibit discernible toponymic evolution patterns over time. Existing quantitative approaches to toponymic studies primarily depend on spatial distribution analysis based on attribute–value frameworks, disregarding the richer semantic and relational contexts underlying name changes. This study proposes a framework that integrates knowledge graphs and large language models (LLMs) to detect, classify, and interpret toponymic evolution patterns. Employing 143 Chinese historical cities as a case study, we construct a multilayered geographic knowledge graph capturing key events and figures, spatial changes, and local customs, thereby developing a knowledge graph–enhanced LLM for semantic extraction, graph retrieval, and pattern identification. The results reveal four distinct types of toponymic evolution: economic prosperity–driven (clustered in coastal and riverine regions and characterized by frequent new additions and abolitions), historical continuity–driven (concentrated in the political cores of successive dynasties and dominated by affiliation and administrative adjustments), natural resource–driven (clustered in regions with distinctive geographical features and characterized by renamings and new additions), and strategic hub–driven (found in vital transport corridors and military centers and characterized by abolitions and seat relocations). This study provides a scalable, data‐driven approach for elucidating the semantic logic underlying place name change and advances research in digital humanities, cultural geography, and spatial governance.
Zeng et al. (Tue,) studied this question.