A Cross-Modal Deep Learning Framework for Quantifying the Complexity of Heritage-Surrounding Environments: A Case Study of the Area Within the Ming City Wall in Xi’an, China

Rapid urbanization is increasingly threatening urban cultural heritage, leading to a systematic imbalance in such heritage and the destruction of its integrity and surrounding environment. The existing assessment methods mostly rely on single-dimensional data or qualitative observations, ignoring the collaborative quantification of the complex state of the heritage-surrounding environment. This study proposes a coupling perspective based on the physical form dimension, functional attribute dimension, and public perception dimension, and constructs a quantitative assessment framework to address the complex state of the heritage-surrounding environment. This framework integrates the spatial perception of SegFormer and the text semantic branch of MacBERT; uses the shared latent space feature alignment mechanism to fuse satellite and street view images, POIs, policy texts, and social media corpora; and, through Bootstrap resampling, builds a three-step adaptive calibration threshold to systematically measure and quantitatively characterize the three indices of spatial form (SMI), functional attribute (FAI), and public meaning drift (PMDI). The empirical evaluation of the Kaitong West Lane corridor reveals pronounced multi-dimensional non-equilibrium spatial evolution characteristics: its physical interface exhibits moderate degradation, and its micro-functional order and commercial ecosystem display severe disorder, yet online public discourse regarding core historical landmarks exhibits low semantic drift. Under dual-anchor conservative criteria, the overall assessment of the study area converges to a “moderately complex state”, while the micro-functional domain independently breaches the severe alert threshold. This framework addresses the perspective limitations of the traditional assessment of the heritage-surrounding environment; realizes the transformation from abstract spatial cognition to computable quantitative models; and thereby provides scientific support for the collaborative protection path and establishes a multimodal computational foundation for multi-dimensional micro-spatial evaluation, providing heuristic guidance for micro-regeneration strategies.