Introduction: This study explores the spatial spillover effects (SSE) and dynamic mechanisms of cultural tourism in the China–Pakistan Economic Corridor (CPEC). This study examines how infrastructure development, cultural integration, and spatial connectivity affect cultural tourism in Pakistan’s CPEC and non-CPEC regions using spatial spillover and Hofstede’s cultural dimensions theory.Methods: Data were collected from 1,387 respondents in both regions, analyzed using the spatial Durbin model and structural equation modeling, and visualized using ArcMap and Geoda.Results: The results indicate that the growth of cultural tourism is strongly influenced by dynamic processes, including better infrastructure, more cultural interchange, and easier accessibility, particularly in CPEC regions. Furthermore, non-CPEC regions exhibit minor advantages in terms of indirect economic gains and increased accessibility, highlighting the ongoing inequalities.Discussion: These findings provide empirical insights into the regional impacts of large-scale initiatives, such as CPEC, and draw attention to government intervention, equitable resource allocation, and stakeholder collaboration to achieve long-term cultural tourism.

This study proposes a decomposed broad learning model to improve the forecasting accuracy for tourism arrivals on Hainan Island in China. With decomposed broad learning, we predicted monthly tourist arrivals from 12 countries to Hainan Island. We compared the actual tourist arrivals to Hainan from the US with the predicted tourist arrivals using three models (FEWT-BL: fuzzy entropy empirical wavelet transform-based broad learning; BL: broad Learning; BPNN: back propagation neural network). The results indicated that US foreigners had the most arrivals in 12 countries, and FEWT-BL had the best performance in forecasting tourism arrivals. In conclusion, we establish a unique model for accurate tourism forecasting that can facilitate decision-making in tourism management, especially at turning points in time.

While cultural authenticity is recognized as central to heritage tourism experiences, the mechanisms through which perceived authenticity influences sustainable tourism behavior remain underexplored. This study develops and empirically tests a serial mediation model examining how perceived cultural authenticity (PCA) affects intergenerational transmission willingness (ITW) and long-term participation intention (LPI) through visitor experience quality (VEQ) and sense of place (SOP). Using survey data from 400 visitors to revolutionary heritage sites in Hainan, China, we employed hierarchical regression and PROCESS Model 6 bootstrap analysis to test seven hypotheses. Results reveal that: (1) PCA significantly influences both VEQ and SOP; (2) VEQ and SOP significantly predict ITW and LPI; and (3) VEQ and SOP serially mediate the PCA–behavioral intention relationship. These findings advance understanding of how authenticity perceptions translate into sustainable heritage tourism outcomes through experiential and affective pathways. Practical implications for heritage site management, focusing on authenticity preservation and experience design, are discussed.

Rural tourism destinations increasingly converge on standardized facilities and packaged landscapes, eroding the place-based distinctiveness that motivates rural travel. Although previous research has distinguished affective and cognitive experiential mechanisms, it remains unclear whether these pathways are differentially associated with sustainability-oriented and loyalty-related behavioral intentions. Addressing this gap, the present study examines how tourists’ perceptions of rural destinations are associated with these two forms of behavioral response. Building on the established distinction between affective and cognitive experiential mechanisms, two perceptual systems are linked to parallel affective and cognitive experiential routes to examine their distinct behavioral associations. An affective route, in which perceived locality and authenticity foster place attachment, and a cognitive route, in which perceptions of planning quality foster tourist satisfaction, are expected to specialize by outcome. Survey data were collected from 411 visitors to rural tourism destinations in Hainan, China, and analyzed using a latent-variable structural equation model with bias-corrected bootstrap tests of competing indirect effects. The results reveal a clear distinction between the two behavioral systems. Locality and authenticity are positively associated with place attachment, which in turn is associated with sustainable behavioral intention, whereas planning quality is positively associated with tourist satisfaction, which is associated with revisit and recommendation intention. The competing-mediation analysis further indicates that locality is associated with sustainable behavioral intention primarily through place attachment rather than satisfaction, and this affective pathway remains consistent across visitor-origin subgroups. Given the cross-sectional design, these relationships should be interpreted as associations rather than evidence of causality. The findings re-conceptualize locality as a measurable tourist perception and demonstrate that sustainability-oriented behavioral intention and loyalty-related intention represent distinct behavioral outcomes associated with different experiential pathways. For destination planners and managers, the findings suggest that strengthening authentic rural character may support sustainability-oriented behaviors, whereas improving the planning and service environment may encourage revisit and recommendation intentions.

Existing self-driving itinerary planning systems rely on single-objective optimization and static cost estimation, limiting their ability to accommodate diverse user preferences and dynamic price fluctuations. Methods: This study presents an interactive itinerary planning system formulated as a Tourist Trip Design Problem (TTDP) with subset selection, integrating an improved NSGA-II algorithm for simultaneous optimization of travel time, cost, and experiential quality, a WPGA-based combined prediction model for dynamic cost forecasting, and a human-in-the-loop interface for real-time preference adjustment. Results: Evaluated on 30 Hainan Island attractions under a 7-day/6-night scenario with 30 independent runs, the improved NSGA-II selects 8.9 ± 1.0 stops with total time 26.2 ± 4.7 h, cost CNY 1526 ± 356, and experience index 36.69 ± 5.60, with backend optimization latency 0.5 ± 0.1 s (end-to-end user-perceived latency including network and frontend rendering: 1.8 s). Compared with standard NSGA-II, the improved version achieves 114.0% higher aggregate experience index while selecting more stops (8.9 vs. 4.2); this improvement is primarily driven by the larger number of selected stops, and normalized indicators (experience per stop: 4.12 vs. 4.08) provide a more balanced interpretation of the trade-offs. The cost model covers three categories (fuel, tickets, and hotel), with the optimization objective f2 capturing route-dependent costs (fuel + tickets) and the hotel treated as a fixed baseline. However, empirical validation is restricted to two geographic corridors.