The development of digital inclusive finance in China can help alleviate the problems of the “three rural areas” and contribute to rural revitalization. The impact of digital inclusive finance on rural revitalization is examined by multiple linear regression, mediated effect model, and threshold effect model in a sample of 30 provinces in China from 2011 to 2020. It is found that (1) the digital inclusive finance index and its sub-indicators - the breadth of digital financial coverage and depth of digital financial use - can promote rural revitalization in China, but the digitalization degree of its sub-indicators does not have a significant linear effect on rural revitalization, but a “U” shaped relationship of first inhibiting and then promoting, with 2017 as the boundary. (2) The regional heterogeneity test reveals that the promotion effect of the digital financial inclusion index and its sub-indicators of digital financial coverage breadth and digital financial usage depth on rural revitalization is more significant in the eastern coastal and central inland provinces, and insignificant in the western remote areas. (3) The mediating effect test finds that digital inclusive finance in China can contribute to rural revitalization by promoting economic growth in rural areas and narrowing the income gap between urban and rural areas. (4) The threshold effect test reveals that there is a single threshold effect of digital inclusive finance and its three sub-indicators on rural revitalization in China. When the digital inclusive finance index and the three sub-dimensions are below the threshold value, they have a facilitating effect on rural revitalization, and after crossing the threshold value, their facilitating effect on rural revitalization becomes more significant. Based on the empirical analysis results, this paper puts forward suggestions for improving the development mechanism of digital inclusive finance, formulating differentiated financial support policies and strengthening financial supervision according to regional differences, and establishing a long-term mechanism for rural revitalization, to promote China’s rural revitalization.

Digital inclusive finance (DIF) plays an active role in preventing poverty-stricken groups from returning to poverty and reducing poverty. This paper empirically tests the impact of DIF on rural poverty alleviation using panel data from 30 Chinese provinces from 2011 to 2020 as a sample. It employs multiple linear regression, mediation effect models, and threshold effect models. The results show that: (1) DIF and its three sub-indicators (coverage breadth, depth of use, and digitalization degree) have significant poverty reduction effects, and the findings hold even when endogeneity is taken into account; (2) a study of regional heterogeneity found that DIF and its sub-indices, coverage and depth of use in the eastern region, have the greatest effect on the poverty alleviation of rural residents, and the effects in the central and western regions have the least effect; (3) the mediation effect test found that DIF could indirectly promote poverty alleviation in rural areas by promoting regional economic growth and narrowing the urban-rural income gap. The Sobel test shows that the mediating effect of regional economic growth is greater than the mediating effect of the urban-rural income gap; (4) it is found through the threshold effect test that regional economic growth has a double threshold effect on rural poverty alleviation, and as the threshold value continues to increase, the poverty reduction effect increases in turn. Therefore, this paper puts forward policy suggestions for the aspects of accelerating the development of DIF in rural areas, implementing regionally differentiated poverty reduction strategies according to local conditions, promoting regional economic growth, and narrowing the urban-rural income gap.

Analyzing the perception differences and influencing factors of cross-cultural groups in heritage tourism can help heritage sites to formulate differentiated service and improve tourist satisfaction. This research adopted the BERT model to undertake sentiment analysis of 17,555 Chinese online reviews for nine scenic spots in Melaka. Using vocabulary filtering, co-occurrence analysis, and semantic clustering technology, the emotional characteristics of Chinese outbound tourists when they visited heritage sites in Melaka were analyzed, which revealed the factors influencing their positive and negative emotions. Results showed that: 1. The BERT-based deep learning approach can obtain improved sentiment predictive performance. 2. Chinese tourists’ general emotional perceptions of Melaka were positive and they were very interested in heritage sites. 3. The most important reason for the negative emotions of Chinese tourists was a lack of cultural experience in Melaka. This research expands the application of deep learning in the field of tourism, and it helps heritage tourism destinations to improve their marketing plans for Chinese tourists and achieve long-term sustainable development of the destination.