Extracting and evaluating typical characteristics of rural revitalization using web text mining

Abstract

AbstractEvaluating typical rural characteristics reveals certain advantages of rural revitalization and is crucial for understanding rural disparities and promoting development. Field research and statistical data can reflect the spatial distribution of local resources and development models. However, due to cost limitations and statistical constraints, it is impossible to effectively compare and evaluate the characteristics of rural development at the long time series, large scale and fine granularity required for sustainable regeneration. This study proposes a web-based method for the extraction and evaluation of rural revitalization characteristics (WERRC). The BERT-BiLSTM-Attention model categorizes rural web texts according to five themes: industrial prosperity, ecological livability, rural civilization, effective governance, and prosperous life. The Term Frequency-Inverse Document Frequency (TF-IDF) algorithm extracts rural characteristics, and the relative advantages of these features are compared among 100 Chinese villages. WERRC extracts the typical characteristics, obtains the spatial distribution and relative advantage, and then ranks them according to the five themes. The relationship between national policy guidance and rural development is explored. The results support further exploration of differentiated, high-quality development modes that incorporate rural advantages into policy, adjust industrial structure, and optimise revitalization strategies at the rural scale.Keywords: Rural revitalizationtypical village characteristicsweb text miningcharacteristic extractionregional sustainable development Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data, codes, and instructions that support the findings of this study are available with the identifier(s) at the private link https://github.com/afxltsbl/Regional-Feature-Extraction.Additional informationFundingThis research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant number XDA23100502 ant the National Natural Science Foundation of China, Grant number 42301523.Notes on contributorsKunkun FanKunkun Fan is a master’s student at the Academy of Digital China (Fujian), Fuzhou University. His primary research interests include web text mining and traffic trajectory data mining. He contributed to the concept, review and analysis of this paper.Daichao LiDaichao Li is currently an associate researcher at the Academy of Digital China (Fujian), Fuzhou University. Her research interests include spatiotemporal data mining, spatiotemporal knowledge graphs, and spatiotemporal data visualization and visual analysis. She contributed to the conception, editing, and review of this paper.Haidong WuHaidong Wu is a lecturer at the School of Economics and Management, Fuzhou University. His research interests include data management and Internet economy and big data analysis. He contributed to the discussion and analysis of this paper.Yingjie WangYingjie Wang an Assistant Professor at the Institute of Geographic Sciences and Resources, Chinese Academy of Sciences. His research interests include tourism GIS, map cartography and GIS, and tourism resource development and planning. He contributed to the analysis and discussion of this paper.Hu YuHu Yu received the Ph.D. degree from the University of Chinese Academy of Sciences. He is currently an associate researcher at the Institute of Geographic Sciences and Resources, Chinese Academy of Sciences. His research interests include tourism geography and ecotourism. He contributed to the review and discussion of this paper.Zhan ZengZhan Zeng is an expert from Hunan Cartographic Publishing House. She contributed to the analysis and conclusions of this paper.