Urban parks are vital components of sustainable cities, providing cultural ecosystem services (CES) that enhance residents’ well-being. Yet understanding how people perceive these intangible benefits remains challenging. This study refines existing CES research by integrating large-scale social media analytics with large language models (LLMs) to capture and interpret public perceptions of parks in Beijing’s core districts. Drawing on more than 46,000 user-generated comments, the LLM-based framework identifies six CES categories (i.e., historical & cultural, aesthetic, recreational, social interaction, educational, and spiritual values) and evaluates their sentiment tendencies. The results reveal that aesthetic value is the most positively perceived dimension, while recreational, educational, and social interaction values show moderate emotional intensity, indicating potential areas for experiential enhancement. Historical & cultural and spiritual values, though less frequently mentioned, generate strong positive affect when recognised. Integrating these findings through Importance-Performance Analysis highlights clear renewal priorities for Beijing’s Garden City initiative. Methodologically, the study demonstrates how LLMs can classify Chinese-language social media text with high accuracy (F1 = 0.8945) and minimal manual annotation, offering a scalable, interpretable, and cost-effective approach for CES assessment. The combined use of social media data, semantic modelling, and performance analysis provides transferable evidence for guiding park management and renewal in other high-density urban contexts.
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