An Approach to Historic District Tourism Experience Based on BERT and TextCNN Deep Learning Models with Web Texts

With the development of the Internet and big data technology, online tourism experience platforms in historic districts have become an important channel for tourists to express their feelings about tourism. However, challenges and problems have emerged in the development of tourism districts, such as balancing conservation and innovation. This improves environmental quality and enhancing tourism attractiveness. This study takes Gao Wang Old Street in Pukou District, Nanjing, as an example to address these issues. By crawling the comment data from the Tiktok platform and applying BERT and TextCNN deep learning models for sentiment analysis, we explored a methodology to study the tourism experience of historic districts based on online text. At the same time, we built a tourism preference prediction model to predict future tourism preferences and demands. The results of the study show that although tourists generally have a positive tourism experience, focusing on tourism activities and positive emotions. They also have negative emotions of uncertainty about the future development of the district and dissatisfaction with environmental conditions. Therefore, the regeneration of historic and cultural neighbourhoods requires integrated planning to promote the sustainable development of the destination and enhance the tourist experience.