It is widely accepted that permeability is a key attribute for sustainable development, but two puzzles remain unsolved. First, there is no consensus on its measurement. Second, there are still many new urban developments that lack this quality; regardless, it is a well-known urban design principle. This paper reviews different permeability measurements and proposes to categorize them into four quadrants, then compares eleven of them with four sets of abstract forms and four real cases from Southeast China. The two measures rooted in space syntax theory demonstrate their robustness in the abstract forms. In contrast, the area-based measures cannot quantify the differences between places with similar content. In the actual case studies, by comparing the differences between original plans and the real built forms, we suggest that three traps impair their permeability and therefore deviate greatly from the initial design visions, resulting in automobile-oriented travel patterns. The two new measures were used to quantify the different levels of permeability in these four cases. It is concluded that the qualitative urban design principles may fail to be delivered without a quantitative and precise description of urban form. Consistent measures and more empirical cases are needed to reveal the contribution of spatial networks to sustainable development.

Artificial intelligence is reshaping approaches to architectural heritage conservation by enabling a deeper understanding of how people perceive and experience historic built environments. This study employs deep learning and large language models (LLMs) to explore public perceptions of the Qinghefang Historical and Cultural District in Hangzhou, illustrating how AI-driven analytics can inform intelligent heritage management and architectural revitalization. Large-scale public online reviews were processed through BERTopic-based clustering to extract thematic structures of experience, while interpretive synthesis was refined using an LLM to identify core perceptual dimensions including Hangzhou Housing & Residential Choice, Hangzhou Urban Tourism & Culture, Hangzhou Food & Dining, and Qinghefang Culture & Creative. Sentiment polarity and emotional intensity were quantified using a fine-tuned BERT model, revealing distinct affective and perceptual patterns across the district’s architectural and cultural spaces. The results demonstrate that AI-based textual analytics can effectively decode human–heritage interactions, offering actionable insights for data-informed conservation, visitors’ experience optimization, and sustainable management of historic districts. This research contributes to the emerging field of AI-driven innovation in architectural heritage by bridging computational intelligence and heritage conservation practice.