Fragmented point-based conservation limits the integrated protection and adaptive reuse of timber arch covered bridges. This study develops a quantitative decision-support workflow for identifying and evaluating heritage corridor networks, using 99 timber arch covered bridges in Northeastern Fujian, China, as the regional sample and three representative bridges for bridge-scale interpretation. The workflow combines spatial pattern analysis, Maximum Entropy (MaxEnt) suitability modeling, XGBoost–SHAP-assisted resistance analysis, circuit theory corridor extraction, complex network analysis, and maturity assessment. The results show that the bridges form a significant clustered pattern with a Shouning primary core and a Pingnan secondary core. MaxEnt identified high-suitability areas of 1.857 × 10⁵ ha, accounting for 3.65% of the study area, mainly in the Shouning–Pingnan–Zhenghe border zone. The XGBoost–SHAP procedure was used as a secondary modeling step to translate suitability patterns into a composite resistance surface; it indicated that road proximity, elevation, nighttime light intensity, and distance to points of interest (POIs) were the main variables shaping the model output. Based on 27 selected heritage source sites, 75 primary corridors totaling 4078.22 km were identified, together with 33 pinch points and 27 barrier points. A corridor protection width of 7 km was determined as optimal. Network analysis identified Luanfeng Bridge, Longtan Bridge, and Denglong Bridge as important structural nodes and divided all 99 bridges into five river basin-related communities. Maturity assessment revealed a pattern of “one core, two belts, and multiple nodes” but also considerable functional disparity. The three representative cases are reframed as preliminary interpretations of model results: Luanfeng Bridge emphasizes landscape-integrity and visitor-pressure assessment, Denglong Bridge emphasizes route continuity and low-intervention interpretation, and Qiancheng Bridge emphasizes livelihood coordination and incremental rural tourism. This study provides decision-making support for advancing the integrated conservation of timber arch covered bridges and the synergistic development of rural tourism.

The heat island effect and air stagnation issues caused by high-density built-up areas are becoming increasingly severe. Optimising urban ventilation structures and establishing ventilation corridors have become key approaches to improving the urban thermal environment and enhancing liveability. However, traditional methods for constructing ventilation corridors often rely on empirical weighting or linear models, which struggle to accurately reveal the resistance coefficients of resistance indicators and fail to reflect the threshold at which indicators transition between positive and negative impacts. Consequently, this study employs Shanghai, China, as a case study, integrating machine learning models with the minimum cost path (MCR) model. Key variables were screened through multiple linear regression and variance inflation factor (VIF) analysis. Subsequently, machine learning models were compared to select the optimal model, with parameter optimisation conducted using Optuna, followed by computational implementation. The results indicate that built environment factors (such as building height, shape complexity, and road density) exert a significantly greater influence on ventilation potential than natural green space factors. By introducing the SHAP method, the positive and negative effects of each indicator on the ventilation environment and their threshold relationships were revealed. Negative indicators were converted into ventilation resistance factors to construct a resistance surface. Building upon this, cold and heat sources were identified using LST, NPP, and population density data. The MCR model was then employed to calculate the minimum resistance paths from cold to heat sources, forming an urban ventilation corridor network. The results indicate that primary corridors align with prevailing wind directions, following urban rivers and low-density green spaces. This study reveals the nonlinear effects of building and green space elements on ventilation systems, proposing machine learning-based optimisation strategies for ventilation corridors. It provides quantitative decision support for mitigating the urban heat island effect and enhancing city livability.