In the context of the in-depth implimentation of the “Digital China” strategy, artificial intelligence (AI) has increasingly become the core engine to drive the high-quality development of tourism. However, at present, the integration of AI and tourism still faces multiple challenges such as the absence of technical ethics, data security risks, and insufficient institutional guarantees, which need to be systematically sorted out and addressed. This paper focuses on the key issues of AI-empowered tourism industry, and systematically analyzes its path mechanism and practical problems in tourism product innovation, service reconstruction, and governance system upgrading. The findings are as follows: (1) AI technology reshapes the operating logic of the tourism industry, promotes the transformation of the industry from element-driven to intelligence-driven, and builds a “data-algorithm-service” loop; (2) As a new production factor, AI is deeply embedded in the function of tourism production, enabling labor enhancement, capital optimization and intelligently resource scheduling through technology; (3) AI drives the integration of culture and tourism into a new stage characterized by digitized resources, immersed experiences, personalized supply and intelligent decision-making, and giving rise to diverse integrated business formats; (4) AI helps build a “government-enterprise-community-tourists” collaborative governance network to promote the transformation of tourism destination governance into an intelligent ecosystem; (5) The risks and challenges in the development of AI are becoming increasingly prominent, and it is necessary to make multidimensional efforts from institutional construction, technical supervision, ethical governance and personnel training to build an inclusive, safe and fair technical governance system. This study helps to clarify the core logic of AI-driven tourism industry transformation, and provides theoretical support and policy suggestions for building a new tourism development model characterized by intelligent co-creation.

The reconstruction of the tourism discipline driven by artificial intelligence (AI) is a core issue in aligning tourism education with China’s national strategy of building a strong education system and supporting the intelligent transformation of the cultural and tourism industries. However, systematic research on how AI can be deeply integrated into the development of the tourism discipline remains limited. This underscores a knowledge gap and the urgent need to integrate AI into tourism education. This paper explores the emerging opportunities, challenges, and paradigms in the AI-driven reconstruction of tourism education. The findings reveal that: (1) AI is reshaping the theoretical foundations of the tourism discipline, fostering interdisciplinary integration and knowledge system reconstruction, thereby catalyzing novel theoretical paradigms that extend tourism research boundaries. (2) AI technologies are expediting the integration of AI into educational practices, leading to profound transformations in curriculum design, instructional methods, and assessment frameworks, in turn redefining teaching roles and enabling more personalized learning experiences. (3) Tourism discipline faces multiple challenges during this transformation, including cognitive misalignment, difficulties in technological implementation, and lagging ethical governance, highlighting the need for unified understanding, robust support for implementation, and updated ethical guidelines. (4) Future discipline development should prioritize technological governance, institutional innovation, and cultural guidance to facilitate intelligent advancement and value recreation in tourism education, ensuring responsible AI adoption, institutional adaptability, and preservation of core cultural values. This study enriches framework for tourism discipline development in the AI era from the theoretical and practical perspective and offers actionable policy recommendations for decisionmakers and educational administrators, contributing to the modernization of a tourism discipline system with Chinese characteristics.

Artificial Intelligence (AI), as a representative technology of the fourth technological revolution, has become a significant driver for promoting Tourism Total Factor Productivity (TTFP) advancement. The study employs the Malmquist index model to measure tourism industry TFP across 30 Chinese provinces from 2011 to 2024, and combines the methods such as Lorenz curves and Getis-Ord Gi* statistical index to compare regional differences in AI technology and explore its impact on TTFP. The results indicate: (1) both AI technology and tourism TFP showed growth trends during 2011-2024, but with notable spatial disparities with AI development in the middle and lower reaches of the Yangtze River and southeastern coastal areas being significantly stronger than the national average, while TTFP in central and northeastern regions is higher than that in eastern and western regions. (2) AI technology has a significant positive impact on tourism technological progress and TTFP enhancement, but its effect on technical efficiency is not evident, but the quantile regression results show that the marginal utility of AI on TTFP first decreases then increases. (3) Regional heterogeneity analysis reveals that eastern, central, and northeastern regions display positive correlations between AI technology and tourism TFP, while effects in western regions have yet to appear. For future development, the tourism industry should: leverage AI technology to develop AI-Generated Content (AIGC) scenarios and enhance interactive experiences with emphasis on “emotional” and “humanistic” orientations; draw from successful experiences in other fields by establishing user profiles and constructing digital twin scenic areas to break low-level cycles; strengthen regional cooperation to promote talent mobility and technology transfer, thereby narrowing regional development gaps and improving TTFP.

Digital economy serves as a powerful driving force for the green development of cultural and tourism industries. Exploring the relationship between digital economy and green technological progress can provide guidance for the sustainable development of cultural and tourism industries. This study takes the panel data of 30 provinces in China from 2013 to 2021 as a sample to analyze the impact mechanism of digital economy on green technological progress in China’s cultural and tourism industries. The results show that: (1) during the study period, the Digital Economy Development Index and the Green Technological Progress Index of the Cultural and Tourism Industries exhibited an overall upward trajectory. However, the digital divide continued to widen, while the Green Technological Progress Index of the Cultural and Tourism Industries is significantly influenced by external environmental shocks; (2) The digital economy exerts a significant and sustained promoting effect on the green technological progress of the cultural and tourism industries; (3) The digital economy promotes the green technological progress of cultural and tourism industries by enhancing the activeness of tourism market entities and increasing the labor input in the tertiary industry; (4) The development level of digital economy and the level of labor skills have heterogeneous impacts on the green technological progress of cultural and tourism industries, and this promoting effect is more obvious in regions with backward digital economy and low labor skill levels. Based on this, the study puts forward relevant suggestions for promoting the green development of cultural and tourism industries from the aspects of giving play to the characteristics of digital economy, strengthening talent training, stimulating the vitality of cultural and tourism markets, and implementing differentiated development strategies.

Cities are important tourist destinations, and smart city construction profoundly affects tourism development. Focusing on 100 prefecture-level pilot smart cities in China, the research employs data from the CEIC database and statistical yearbooks of those prefecture-level cities from 2006-2019 to analyze the impact of smart city construction on tourism development level through difference-in-differences model and panel data regression model. Research has found that: (1) smart city construction has significantly improved tourism development level. (2) Smart city construction mainly promotes the improvement of tourism development level from four dimensions, namely, smart infrastructure, smart transportation, technological innovation, and smart economy, among them, technological innovation dimension shows the strongest influence. (3) The impact of the four dimensions of smart city construction on the tourism development level varies according to the size of the city. The dimensions of technological innovation, smart infrastructure, and smart economy have the most prominent impacts in large cities, medium-sized cities, and small cities, respectively. These findings provide empirical evidence and theoretical support for optimizing smart city initiatives to drive high-quality tourism development.

This study investigates the New Quality Productive Forces (NQPF) and tourism industrial competitiveness across 272 Chinese cities based on the entropy weight TOPSIS method to measure the coupling degree and the coordination degree between new quality productivity and tourism industrial competitiveness, and to explore the dynamic mechanism of the coordination degree using the panel data. The findings reveal that: (1) the level of NQPF is lower than the level of tourism industrial competitiveness, but the growth of the former is higher than that of the latter. Significant disparities exist among cities, with the eastern region leading in both NQPF and tourism industrial competitiveness, though growth trends remain similar across regions. (2) The overall level of coupling and coupling coordination between NQPF and tourism industrial competitiveness is moderate but shows an increasing trend year by year. There is no significant difference in the level of coupling between eastern, central and western cities, but the coupling coordination degree of eastern cities is higher than that of central and western cities. (3) Digital innovation, fiscal expenditure, urbanization, marketization, and resident consumption are the fundamental drivers for the coupled and coordinated development of new quality productivity and tourism industrial competitiveness. Digital innovation is the core driving mechanism for the coordinated development of new quality productivity and tourism industrial competitiveness in eastern, central and western cities, with eastern cities relying mainly on market power, central cities relying on the dual power of government and market, and western cities being driven by both government power and market power. In addition, high-innovation cities rely on endogenous and market dynamics, low-innovation cities are significantly influenced by exogenous dynamics, while tourism-dependent cities have a single driving mechanism, and non-tourism-dependent cities show diversified driving characteristics. These findings expand application scenarios of the theoretical framework of NQPF, deepen the understanding of its interplay with tourism development, and provide references for the synergistic enhancement of the competitiveness of new quality productivity and tourism as well as the promotion of the construction of the emerging strategic pillar industry of tourism.

The digital transformation of the tourism industry has changed the operational mode of tourist destinations and provided local residents the opportunities to participate in tourism and challenges to their pro-tourism behaviors Based on the Ability-Motivation-Opportunity (AMO) theory, this study employs Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze 297 questionnaires from residents of Pingyao Ancient City, revealing the impacts of digital literacy (digital technology, digital cognition, digital social emotion) and perceptions of economic, environmental, and socio-cultural benefits on pro-tourism behavior in configuration path analysis. The study finds that:(1) no single factor constitutes a necessary condition for pro-tourism behavior, with environmental benefit perception being the first bottleneck factor. (2) Pro-tourism behavior exhibits “multiple concurrency” and “different means but same result” effects, with four high pro-tourism behavior patterns (pure digital-driven, technology-economic-emotion, digital-economic, and comprehensive types) and four non-high pro-tourism behavior patterns (environment-emotion constrained, environment constrained, digital technology deficient, and digital social emotion insufficient types). (3) The antecedent conditions influencing high and non-high pro-tourism behavior have heterogeneity and certain substitutability. The study clarifies the multiple antecedent relationships, causal asymmetry, and multiple equivalent pathways of residents’ pro-tourism behavior, providing theoretical support for the sustainable development of tourism destinations and practical guidance for policymaking and resource allocation.

The scientific distribution of digital tourism signage has become a critical issue in enhancing urban tourism experiences and the efficiency of public information services. However, most existing site selection approaches still rely heavily on empirical judgment, often resulting in uneven information distribution and unbalanced signage density, which negatively impacts tourists’ access to attraction information and overall satisfaction. To address this problem, this study proposes a location optimization model for digital tourism signage based on the objectives of maximal coverage, and introduces deep reinforcement learning to solve the model with support from multi-source spatiotemporal data. The research includes a detailed process of data collection and preprocessing, integrates the Geo-detector and attention mechanism to identify key influencing factors, and constructs the state space and decision strategy through deep reinforcement learning. Additionally, the proposed method is systematically compared with classical solvers and heuristic algorithms in terms of solution quality and computational efficiency. Using the Beijing Fifth Ring Road Area as the experimental case, the results show that: (1) existing digital tourism signs are primarily concentrated in the eastern and northeastern regions, highly correlated with the spatial distribution of tourist attractions; (2) Tourism heat, visitor activity, and public transportation accessibility significantly influence the signage distribution; (3) All three optimization methods prioritize the layout of signage in Xicheng District and its surrounding areas, with the deep reinforcement learning approach achieving better computational efficiency while ensuring layout effectiveness. This study innovatively applies deep reinforcement learning to the digital tourism signage location problem, promoting the transformation of location strategy from experience-driven to data-driven, and from static deployment to intelligent optimization. It provides both a technical foundation and practical reference for the smart planning of tourism information infrastructure.

Scientific measurement of the spatial matching relationship between tourist resource points and tourist interest points is a key technical link in the selection of routes for self-driving scenic byways and the optimization of regional tourism productivity layout. The study introduces the theory of spatial matching, integrates multivariate data, and tries to construct the Spatial Matching for Scenic Byway (SMSB). Firstly, GIS analysis is used to identify the spatial clustering characteristics and distribution patterns of tourist resource points and tourist interest points, and the spatial matching measurement model is used to evaluate the spatial matching degree of the two and classify the matching types. Secondly, considering terrain slope, land use type, ecological sensitivity and other limiting factors, we constructed the suitability evaluation index system of scenic route selection through weighted superposition analysis, and divided the spatial suitability zoning. Again, the data of self-driving tourists' points of interest, OSM road network and tourist resource points are sequentially rasterized and superimposed to generate a node raster, and the spatial nodes are screened out in combination with the assessment of the current situation. Finally, based on the results of the above three steps-spatial matching degree, spatial suitability and spatial nodes-scenic route selection planning of the self-drive tour is carried out on the basis of the existing road network. The empirical test in Western Fujian found that the SMSB routing method overcomes the limitations of the traditional scenic route slecetion method that relies on a single distribution of tourism resources or tourist interest points, makes up for the lack of quantitative assessment of the relationship between the spatial distribution of tourism resources and the demand preferences of the self-driving tourists in the existing studies, simplifies the complexity of the spatial node selection procedure, and enhances the scientificity and accuracy of the scenic route routing.

The core purpose of national parks lies in maintaining the integrity of natural ecosystems. Nevertheless, light pollution has become a new challenge currently for natural ecological conservation in national parks. The research takes the Giant Panda National Park as an example. Based on long-term nocturnal light remote sensing data, in combination with the spatial analysis technology of Geographic Information System and the geographical detector method, it explores the spatiotemporal evolution characteristics and influencing factors of light pollution from 1984 to 2020. The research findings indicate that: (1) in terms of temporal evolution, light pollution in the Giant Panda National Park is generally on the rise, with a significantly intensification observed particularly after 2000 and 2008. (2) In terms of spatial differentiation, the pollution exhibits a characteristic pattern of “peripheral agglomeration and core infiltration”, where areas of significant change are concentrated along the fragmented edges of the park, adjacent to major transportation corridors, and near administrative boundaries. (3) In terms of influencing factors, light pollution is driven by the nonlinear interaction of multiple factors. Among them, the gross domestic product is the leading factor, and when it is superimposed with the road network density, the impact reaches its maximum. This research reveals the spatiotemporal evolution patterns and influencing factors of light pollution in the Giant Panda National Park, providing an important reference for the formulation of protection and construction policies for national parks.

The spatial nesting relationship between tourism resources and surrounding polluting enterprises is an important factor affecting the sustainable development of tourism. Using exploratory spatial data analysis and co-location quotient, this paper discusses the spatial association characteristics and patterns between tourism resources and polluting enterprises in Fujian Province. The results show that: (1) in the overall pattern, tourism resources and polluting enterprises in Fujian Province show a significant spatial mismatch, and the region with high diversity and high uniformity of tourism resources is the region with low diversity and low uniformity of polluting enterprises; on the clustering characteristics, the high-density centers of tourism resources and polluting enterprises show a pattern of contiguous clustering in coastal areas and counties. (2) In terms of spatial association characteristics, the overall spatial association between tourism resources and polluting enterprises is weak, and the spatial correlation intensity between natural and cultural tourism resources and polluting enterprises has spatial differences; the highly synergetic areas between natural tourism resources and manufacturing, wholesale and retail enterprises are concentrated in southern Fujian and northeast Fujian; and the highly synergetic areas of cultural tourism resources and agriculture, forestry, animal husbandry and fishery enterprises are the tourism areas of the southwest and northwest Fujian. (3) On the spatial association patterns, tourism resources and polluting enterprises mainly form four spatial association patterns: high-high-remote type, low-low-non-remote type, high-low-non-remote type and low-high-remote type; in the remote type, the polluting enterprises tend to be far away from the administrative center, while in the non-remote type, the polluting enterprises do not deviate significantly from the administrative center. The study reveals the spatial relationship between tourism resources and polluting enterprises from the geographical level, which is of great significance for the analysis of the spatial layout of tourism resources and surrounding industries.

Health and wellness activities are a form of temporary settlement behavior, and the selection those destinations is significantly influenced by the long-term climate characteristics of the area. This study utilized meteorological data from 54 grid nodes in Anhui Province from 1991 to 2020, processed into typical meteorological data for Solar Terms, to analyze the spatiotemporal distribution characteristics of the wellness climate in Anhui Province. The study also conducted a zoning analysis of the suitability of the wellness climate using clustering methods. Additionally, data from 8 grid nodes outside Anhui Province were selected for comparative analysis. The results indicate: (1) Spring and autumn are the most suitable seasons for health and wellness in Anhui Province, with some areas suitable for summer retreats, while the winter comfort level throughout the province is relatively low; (2) Yuxi and Shexian County have the highest suitability for health and wellness, while southwestern and southern Anhui generally perform well; (3) Anhui Province can be divided into four climate characteristic zones suitable for wellness, which intuitively reflect the impact of latitude and local geography on the variability of health and wellness climate resources; (4) Compared to some northern cities, a few areas in northern Anhui have health and wellness advantages; compared to the eastern and southern neighboring regions of Anhui Province, southwestern and southern Anhui have health and wellness advantages; compared to southern cities, Anhui Province has a longer period of suitability for health and wellness, with southwestern Anhui having potential for off-peak development; (5) Under the backdrop of climate change, the suitable zones for health and wellness climate are generally shifting northward. The study innovatively proposed the use of typical meteorological data from solar terms for evaluating the suitability of the health and wellness climate, systematically revealing the spatiotemporal distribution patterns of the suitability of the health and wellness climate, addressing the issue of temporal roughness in traditional mean or threshold evaluations, and is of significant importance for the scientific evaluation of health and wellness climate resources and the development of the health and wellness tourism industry.

In the context of efficiency change and economic transformation, promoting the coordinated development of cultural industry and tourism industry efficiency has become crucial for achieving cultural and tourism integration and high-quality development. This study establishes an analytical framework for the synergistic growth of the efficiency, and systematically investigate the characteristics of cultural-tourism efficiency coordination and its influencing factors in China by employing the methods of the Super-EBM model, Haken model, non-parametric kernel density estimation, Theil index, and fuzzy-set Qualitative Comparative Analysis (fsQCA). The study finds: (1) both cultural industry efficiency and tourism industry efficiency exhibit positive feedback effects on the synergistic growth system between cultural and tourism industries, yet the cultural industry efficiency dominates the evolution system. (2) During the study period, the synergistic growth of the efficiency between cultural and the tourism industries in general shows the diffusion characteristic of gradually decreasing from the southeast coast to the northwest inland, forming a regional hierarchy of “Central China > Eastern China > Western China”. (3) Kernel density estimation identifies a persistent “single-peak” distribution pattern across national and regional levels, while Theil index decomposition reveals an overall “U-shaped” evolutionary trajectory primarily attributable to inter-regional disparities. (4)The synergistic growth of the efficiency between cultural and tourism industries is influenced by multiple factors, and fsQCA identifies four distinct configuration paths: digital economy-human capital synergy, government-resource synergy, market-transportation synergy, and a compound “government-resource+market-transportation” model. The article reveals the complex grouping causes of the synergistic growth of the efficiency between cultural and tourism industries, enriches the research on the relationship between culture and tourism, and.offers theoretical references, in-depth integration and high-quality development of culture and tourism.

In the Internet era, network attention is a key way to obtain industrial tourism information, reconfigure inter-industry interactions and connections, and to a certain extent, help the city’s high-quality development. Based on the Baidu index of industrial tourism network attention of 136 prefecture-level cities in the “Third Front Construction” area from 2011 to 2022, the spatial and temporal evolution characteristics of industrial tourism network attention and urban high-quality development are visualised by using ArcGIS 10.6 software, and comprehensively analysed by using the spatial Durbin model and threshold model to analyse the spatial spillover effect and non-linear effect of industrial tourism network attention on the high-quality development of cities in the “Third Front Construction” area. The study found that: (1) the industrial tourism network attention and high-quality development index of cities in the “Third Front Construction” show the characteristics of increasing year by year in chronological order, but the industrial tourism network attention fluctuates and changes in some cities; in spatial order, it shows the characteristics of “point by point, piece by piece development”, and there exists a significant spatial correlation. (2) Improvement of industrial tourism network attention can promote the overall high-quality development of cities in the “Third Front Construction” Area, but there is a negative spatial spillover effect. In the spatial effect, the local effect is greater than the spatial spillover effect. (3) Industrial tourism network attention demonstrates geographical variations in driving high-quality development of cities within “Third Front Construction” Area. Central and eastern cities show local impacts, while western cities simultaneously exhibit positive local effects and negative spatial spillover effects. (4) The promotion effect of industrial tourism network attention on high-quality development of cities in the “Third Front Construction” Area will be enhanced with the increase of Internet penetration and the continuous upgrading of industrial structure, with a significant double-threshold effect. The results of the study are of great significance and value in grasping the attention of industrial tourism network and promoting the high-quality development of the cities in the “Third Front Construction” area from a macroscopic perspective.