Understanding the relationship between environmental features and perceptions of urban green spaces (UGS) is crucial for UGS design and management. However, quantifying park perceptions on a large spatial and temporal scale is challenging, and it remains unclear which environmental features lead to different perceptions in cross-cultural comparisons. This study addressed this issue by collecting 11,782 valid social media comments and photos covering 36 UGSs from 2020 to 2022 using a Python 3.6-based crawler. Natural language processing and image recognition methods from Google were then utilized to quantify UGS perceptions. This study obtained 32 high-frequency feature words through sentiment analysis and quantified 17 environmental feature factors that emerged using object and scene recognition techniques for photos. The results show that users generally perceive Japanese UGSs as more positive than Chinese UGSs. Chinese UGS users prioritize plant green design and UGS user density, whereas Japanese UGS focuses on integrating specific cultural elements. Therefore, when designing and managing urban greenspace systems, local environmental and cultural characteristics must be considered to meet the needs of residents and visitors. This study offers a replicable and systematic approach for researchers investigating the utilization of UGS on a global scale.

To understand how landscapes have changed in Yanqing District, Beijing, during its urban development over the past 15 years, we referred to the Landscape Character Assessment (LCA) theory, selecting altitude, slope, roughness, forest type, land cover, and forest vegetation cover as characteristic factors, identified nine types of landscape character types (LCTs) from 2005 to 2020 through unsupervised clustering. Then, we applied multi-model interpreters, including the Optimal Parameter-Based Geographical Detector (OPGD) and SHapley Additive exPlanations (SHAP), to analyze how social and natural factors impact the spatiotemporal changes of these LCTs. The results indicate that over the past 15 years, the landscape character of Yanqing District has undergone significant changes, with more frequent changes occurring in the “piedmont” areas where mountains meet plains. Slope and precipitation are the main factors affecting the intensity of LCT changes. In contrast, the transformation of different landscape characters is affected by factors such as altitude, slope, precipitation, and distance to artificial surfaces. This study reveals the dynamic changes in landscape character and their driving mechanisms, helping to develop more targeted strategies for landscape management in Yanqing District to promote sustainable regional development.