With the rapid development of urbanization and modernization, the population of traditional villages migrates into surrounding areas, causing the hollowing of traditional villages. The disintegration of China’s traditional village means the loss of historical memory and cultural characteristics of ethnic regions, seriously endangering the country’s cultural heritage. To better understand the hollowing phenomenon, this study analyzed 2645 villages from the Chinese traditional village catalogue (Batch 5) and evaluated different village attributes, including location, household registration, permanent population, number of traditional buildings, cultural relics, historical buildings, and non-heritage representative projects. We constructed an evaluation index system and used the entropy weight method, comprehensive evaluation method, and correlation analysis method to quantitatively assess the characteristics and influencing factors of hollowing among traditional Chinese villages. The main results are as follows: ① The hollowing index was above 0.5; most traditional villages have entered the stage of high hollowing. ② The traditional villages with hollowing index above 0.9 comprised 92%, and those between 0.8 and 0.9 made up 6%. Those with hollowing index at intervals 0.7–0.8, 0.6–0.7, and 0.5–0.6 accounted for 0.98%, 0.30%, and 0.11%, respectively. ③ Population hollowing is the fundamental cause of traditional village hollowing. In more than 99% of traditional villages, the population hollowing index was greater than 0.7. ④ More than 99% of traditional villages have a building hollowing index greater than 0.4, and more than 92% of the villages had a per capita number below 0.1. ⑤ The cultural hollowing rate for most traditional villages was very high. The cultural hollowing index for more than 99% of traditional villages was greater than 0.7. This study provides references for government administrators and scholars in rural revitalization and traditional village hollowing governance.

Tourism flow is an important monitoring indicator for the industrial operation of ecotourism destinations, crucial in its planning, development, and marketing. Based on the travel journal data of popular tourism websites, this paper analyzed the spatial and temporal distribution, network structure, and the coupling characteristics between ecotourism flow and traffic accessibility in Zhangjiajie using standard deviation ellipse, social network analysis, kernel density estimation, and a spatial coupling model. The main results are as follows: (1) In terms of time distribution, the ecotourism flow in Zhangjiajie is mainly short-term tourism, with significant seasonal differences. (2) In terms of spatial structure, the ecotourism flow in Zhangjiajie presents a polygonal distribution of “core-edge”, forming a dense distribution area of Wulingyuan–Tianmen Mountain–Huanglongdong ecotourism flow. (3) In terms of spatial and temporal distribution, the connection intensity between the core area of ecotourism flow in Zhangjiajie and other scenic spots increases as the tourism length of stay increases. However, the radiation scope is mainly in the surrounding ecotourism scenic spots. (4) The coupling degree of ecotourism flow and traffic accessibility in Zhangjiajie is high, but the coupling coordination degree remains low on the whole. In addition, there is a serious mismatch between ecotourism flow and traffic accessibility in Zhangjiajie.

Ecologically fragile areas in China account for more than half of its land area. Performing early warning assessments and trend analyses of resource and environment carrying capacity in ecologically fragile areas can lay a scientific foundation for ecological conservation in the areas. Based on the connotation of resource and environment carrying capacity, an early warning index system of resource and environment carrying capacity in Altay prefecture was constructed from the three aspects natural resource carrying capacity, eco-environment carrying capacity, and economic and social support capacity. The grey relational projection method model was used to analyze the current alarm situation of the resource and environment carrying capacity in Altay prefecture from 2011 to 2020, and then the back propagation (BP) neural network and a mathematical statistics software were used to predict the evolution of the alarm situation of the resource and environment carrying capacity in Altay prefecture from 2021 to 2025. The results demonstrated that (1) the natural resource carrying capacity subsystem was the main system of the development of the resource and environment carrying capacity in Altay prefecture, and its impact on the resource and environment carrying capacity in Altay prefecture was greater than the eco-environment carrying capacity and economic and social support capacity; (2) the resource and environmental carrying capacity of Altay prefecture showed a slight upward trend from 2011 to 2020, although the range was constrained and the level of warning remained “moderate warning”. A spatial pattern of “weak in the middle, strong in the two poles” was exhibited by the warning scenario about the carrying capacity of each county and city. Except for the warning of Habahe County and Qinghe County, where the warning was slightly worse than that in 2020, the warning of resource and environment carrying capacity in Altay prefecture and other counties and cities would show a trend of fluctuation and decline from 2021 to 2025. However, the degree of alarm did not change substantially and remained at the level of “moderate warning”; (3) the main factors restricting the mitigation of the warning of resource and environment carrying capacity in Altay prefecture included a low soil fertility index, a small total reservoir capacity, low per capita mineral resource reserves, a low water resource development and utilization rate, a low comprehensive utilization rate of industrial solid waste, and a low land output rate.