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This study introduces an automatic classification method based on machine learning. We selected 10 popular and successfully developed ancient towns in China as case studies. Using online tourist reviews as our data source, we first applied the Latent Dirichlet Allocation (LDA) topic model to identify the core elements of cultural heritage. Following this, we used the Naive Bayes classification method to distinguish between positive and negative tourist comments. From the perspective of the tourist experience, our results show that the evaluation of ancient town cultural heritage development depends on 8 key indicators, which consist of 20 specific aspects. The eight indicators are: Differentiated Landscape Experience, Architectural Atmosphere Experience, Diversity of Experience, Night-time Experience, Cultural Atmosphere Experience, Spatial Atmosphere Experience, Personal Needs, and Consumption Satisfaction. Among these, Differentiated Landscape Experience was identified as the most important factor, followed by Personal Needs. Personal Needs also represented the most positive indicator, while Consumption Satisfaction was the most negative. The research proposes and validates an evaluation framework that deeply integrates machine learning with expert knowledge, overcoming the disadvantages of manual coding, which is labor-intensive and highly subjective. Additionally, this is the first study to investigate the common elements across multiple ancient towns. The findings provide guidance for the preservation and development of cultural heritage in ancient towns and offer a valuable reference for future research.<\/jats:p>","DOI":"10.1145\/3797031","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T12:05:16Z","timestamp":1770897916000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantitative Evaluation of Cultural Heritage Development in Ancient Towns from the Perspective of Tourist Experience"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4659-5225","authenticated-orcid":false,"given":"Lei","family":"Yu","sequence":"first","affiliation":[{"name":"School of Arts and Design, Yanshan University, Qinghuangdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0513-0512","authenticated-orcid":false,"given":"Yuxiang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Arts and Design, Yanshan University, Qinghuangdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9267-062X","authenticated-orcid":false,"given":"Guoqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Arts and Design, Yanshan University, Qinghuangdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"issue":"4","key":"e_1_3_2_2_2","first-page":"97","article-title":"Welcome to the experience economy","volume":"76","author":"Pine B. 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