A Landscape Narrative Model for Visitor Satisfaction Prediction in the Living Preservation of Urban Historic Parks: A Machine-Learning Approach

Abstract

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.

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