Ecotourism is increasingly recognized as a vital component of sustainable development within national parks, yet its potential within China’s emerging national park system remains underexplored. Focusing on the Shanxi Province section of the Taihang Mountain National Park—a candidate area designated in December 2022—this study addresses the critical questions of whether and how to develop ecotourism tailored to this region’s unique environmental and cultural characteristics. To address these issues, a mixed-method approach integrating quantitative and qualitative analyses was employed to conduct a comprehensive assessment of the area’s tourism resources and market potential. A statistical analysis of its ecotourism resources and a high-frequency word cloud analysis of online travelogues revealed that the region possesses significant potential for ecotourism development. These findings provide actionable strategies, including fostering gateway communities, improving infrastructure, and enhancing tourism services, while emphasizing the importance of community involvement, low-carbon practices, and sector diversification. This study not only advances our theoretical understanding of ecotourism by highlighting the role of gateway communities and the balance between conservation and tourism growth but also offers practical insights for the sustainable development of China’s national parks.

Shalimar Garden, a UNESCO World Heritage Site, showcases Mughal engineering but underutilizes its educational potential. A mixed-methods study (n=437; surveys, inter-views) found that independent visitors are less satisfied (60.1±3.8%) than guided groups (71.3±3.2%; p=0.004), and youth (≤35 years) are less engaged (35.2±3.7%). South Asian visitors are very culturally engaged (>80%; OR=1.87, p=0.012), while interna-tional tourists suffer accessibility difficulties (18.7%; β=−0.61, p< 0.001). Post-2020 digi-tal initiatives enhanced visits (28.4±3.1% yearly growth; r=0.79), while seasonality (+41.2% Q3–Q4) and linguistic gaps (AOR=2.11, p=0.002) persisted. Our Algorithmic Interpretation Framework combines generative AI (Δ+22–28% participation), interna-tional networks, and community archives to promote SDG 11.4 compliance. It pro-vides scalable, tech-driven UNESCO site solutions.