Abstract
Space syntax analysis technology is an intelligent urban technology that offers a distinctive viewpoint on the arrangement and configuration of exhibition areas within museums. This study explores the application of Space Syntax theory in optimizing museum design management, taking Foshan Ancestral Temple Museum as a case. Through axis analysis and line of sight simulation, combined with core indicators such as connectivity, integration, and harmonic average depth, the spatial structure characteristics and crowd flow patterns are revealed. The study found that the Lingying Archway-Wanfutai axis and the nearby Jinxiang Pond area were very effective in terms of integration, connectivity, and intensity, making them the main center of the spatial network, with good visibility and a strong ability to attract crowds, while places like Wu’an Pavilion had poor visibility and deep spaces, leading to underused areas. The results show that space syntax can effectively identify core nodes and inefficient areas in the complex space of traditional buildings, providing a scientific basis for optimizing path guidance and activating edge spaces. It is recommended to enhance the accessibility of edge exhibition areas by enhancing the composite functions around Jinxiang Pond and optimizing the line-of-sight corridors and improving the emergency evacuation design for high-traffic areas. Although the study is limited by spatial data simplification and behavioral pattern assumptions, its combination of quantitative analysis and spatial narrative methods provides a methodological framework for reference for the spatial activation and modern design management of Lingnan traditional architectural museums. In the future, it is necessary to deepen the research through multi-source data fusion and dynamic monitoring.
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Gan, Z., Song, J., Chen, Y., Zheng, L., & Tang, Q. (2025). Promoting Design Management and Spatial Structure Optimization in Foshan Ancestral Temple Museum Based on Space Syntax. In Proceedings of 2025 International Conference on Management Science and Computer Engineering, MSCE 2025 (pp. 732–738). Association for Computing Machinery, Inc. https://doi.org/10.1145/3760023.3760137
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