Evaluation of tourism elements in historical and cultural blocks using machine learning: a case study of Taiping Street in Hunan Province

Historic districts, as core areas in cities rich in historical, cultural, and aesthetic resources, offer diverse tourism services to the public. The development of tourism has become a global trend in revitalizing these districts, and evaluating the tourism elements within them provides an objective and accurate basis for decision-making in their multifunctional planning. However, traditional evaluation methods often lack the precision and comprehensive coverage necessary for fine-grained, dynamic studies of tourism elements in historic districts. To address this issue, the present study proposes an evaluation model based on machine learning. Cluster analysis of comment texts is conducted through LDA topic classification, and sentiment analysis is performed using the BERT model to extract key indicators and sentiment classifications that affect tourism element evaluation. Additionally, IPA analysis is utilized to explore the relationship between the importance of tourism elements in historic districts and tourist satisfaction. The main contribution of this study is the development of a quantitative and replicable evaluation model for tourism elements, providing a scientific foundation for both tourism development and heritage preservation. The results reveal several key findings: (1) Regional function (RF) is the dominant factor in evaluating tourism elements, while tourists have a weaker perception of management and service (MS), which has the lowest weight. (2) A significant interrelationship exists among topic keywords, with historical culture, the built environment, and local characteristics being interdependent and integrated. (3) Tourism experience (TE) and historic culture (HC) received the highest positive ratings, while RF and space accessibility (SA) accounted for a greater proportion of negative feedback. (4) Despite the high importance of RF, although RF is the most important, it has a low satisfaction among tourists and is a key factor that needs to be improved. These findings deepen our understanding of key tourism elements in historic districts and offer fresh perspectives for future research on historic districts the evaluation and sustainable development of historic districts.