The health status of ecosystems is an important prerequisite for ensuring regional ecological security. Exploring the spatiotemporal patterns, driving mechanisms, and zoning regulation pathways of ecosystem health is of great significance for achieving co-ordinated and sustainable regional ecosystems. This study uses China as a case area and applies the InVEST model to measure integrated ecosystem services and incorporates it into an evaluation framework for ecosystem health based on the “Vigor-Organization-Resilience-Ecosystem Services” (VORS) model. It reveals the spatiotemporal evolution characteristics of ecosystem health in China from 2000 to 2020 and employs the geodetector and spatiotemporal geographically weighted regression model to analyze the main influencing factors and spatial differentiation characteristics, thereby exploring ecological management zoning and optimization pathways. The study results show that (1) during the study period, the overall ecosystem health level in China showed a declining trend, dropping from 0.397 in 2000 to 0.377 in 2020. (2) Overall, China’s ecosystem health exhibits strong spatial positive correlation and spatial clustering characteristics, with a basic pattern of lower values in the northwest and higher values in the southeast. (3) Vegetation coverage, population density, density of road network, and per capita GDP are the main influencing factors of ecosystem health in China. (4) China is divided into five types of Ecological Management Zones: Ecological Conservation Zone, Ecological Enhancement Zone, Ecological Buffer Zone, Ecological Remediation Zone, and Ecological Reshaping Zone, with differentiated strategies proposed for optimizing ecosystem health in each zone.

Although artificial intelligence (AI) is extensively applied in tourism, empirical insights into its nuanced impacts on specific work tasks are still emerging. Utilizing Chinese General Social Survey data (2010–2023), this paper examines how AI is associated with different task types and explores the underlying mechanisms. Results suggest that AI is associated with an emerging shift in the occupational composition among tourism practitioners, leaning toward greater routine cognitive task intensity and lower non-routine cognitive task intensity—primarily in hospitality and travel agencies—while no significant effects are observed on manual tasks. Mediation analyses indicate that reduced job autonomy represents a potential pathway associated with this adjustment. Further analysis shows that this constraint on job autonomy exhibits significant nonlinear and dynamic characteristics: it strengthens after AI penetration crosses a specific threshold and demonstrates a staged temporal evolution—transitioning from early assistance to subsequent constraint before stabilizing. We attempt to conceptualize this phenomenon as the “re-routinization” of tourism employment, whereby AI is associated with greater routine cognitive task intensity and lower non-routine cognitive task intensity, with reduced job autonomy emerging as a potential partial pathway.