Urban historic parks face the dual challenge of achieving the living preservation of historic buildings while enhancing contemporary visitor satisfaction. In the context of accelerating urbanization and growing demand for immersive cultural experiences, it is increasingly important to conserve historical and cultural values while maintaining relevance and emotional engagement. This study adopts a mixed-methods approach to develop a predictive model for visitor satisfaction within the framework of living preservation, using Yingzhou West Lake in Fuyang City, Anhui Province, as a representative case. Qualitative methods were employed to identify key landscape narrative dimensions, while quantitative data from structured questionnaires highlighted critical experiential elements such as environmental restoration perception, flow experience, and cultural identity. Three machine-learning algorithms—random forest, Support Vector Machine (SVM), and XGBoost—were applied, with the most accurate model used to analyze the relative contribution of each component to visitor satisfaction. The findings revealed that immersive experiential elements play a central role in shaping satisfaction, while physical and cultural elements, particularly historic buildings and their contextual integration, provide essential structural and emotional support. This study offers data-driven insights for the adaptive reuse and interpretive activation of historic architecture, proposing practical strategies to harmonize cultural continuity with visitor engagement in the sustainable management of urban historic parks.

This study is grounded in the global agenda of achieving the Sustainable Development Goals (SDGs), emphasizing the critical role of historic cultural heritage buildings in fostering cultural continuity and long-term engagement. Centered on the living conservation of architectural heritage, the research explores how immersive and embodied interactions shape visitor loyalty. Structural equation modeling (SEM) demonstrates that environmental restoration and flow experience significantly mediate the relationship between embodied experience and loyalty, while cultural identity further strengthens these pathways. To complement the explanatory analysis, deep learning methods were employed, with the multilayer perceptron (MLP) outperforming the gated recurrent unit (GRU) in terms of predictive accuracy. SHAP analysis revealed that technological mediation and physical interaction are the strongest predictors of loyalty, followed by sensory immersion. By integrating explanatory and predictive perspectives, the study refines the embodied experience framework and offers both theoretical insights and practical guidance for the design and conservation of historic cultural heritage buildings. Ultimately, the findings highlight that embodied experience-driven living heritage conservation not only sustains cultural identity but also makes a meaningful contribution to the realization of global sustainable development.

Heritage-led renewal can improve the built environment of historic districts, but physical upgrading may not be accompanied by better public-space experiences or stronger place attachment among residents. However, comparative evidence remains limited on how different aspects of public-space experience relate to Place Identity (PI) and Place Dependence (PD). Taking Nantou Ancient City in Shenzhen as a case study, this research combined a survey of 780 residents with field observations and used Random Forest, XGBoost and out-of-fold SHAP to assess the relative predictive importance of Public-Space Governance, Perceived Accessibility and Social Interaction for PI and PD. The results show that (1) PI was most strongly associated with Social Interaction in XGBoost, which had the highest domain-level importance (46.5%), while Collective Participation, Informal Governance, Strong-Tie Interaction and Formal Governance were the leading dimensions; (2) PD was more closely related to functional and use conditions, with Perceived Accessibility showing the highest domain-level importance (44.5%), and with Perceived Physical Accessibility, Formal Governance and Perceived Social Accessibility the three highest-ranked dimensions for PD; and (3) Public-Space Governance showed substantial relative predictive importance for both PI and PD. Random Forest showed the same domain rankings as XGBoost for both outcomes and demonstrated high consistency at the indicator level. These results indicate that PI and PD are associated with different aspects of public space experience, but the causal relationship still lies beyond the scope of this cross-sectional study.