博物馆是一种重要的文化和教育机构，在传承国家文化基因、
促进文明交流互鉴方面起到重要作用，同时也为公众提供了丰富多
彩、有趣的参观和学习体验。然而，博物馆的服务质量和用户体验
对于吸引和留住游客至关重要。因此，通过分析游客反馈，明晰我
国历史博物馆发展中亟需提升的方面，有助于博物馆管理者发现存
在的问题和改进的空间，提高博物馆的服务质量和用户体验。
但目前学术界对于博物馆满意度的研究方法存在一定不足，少
有研究者通过文本挖掘来分析用户意见，尤其是能够细粒度的挖掘
用户评论中的评价属性和情感信息。除此之外，以往研究通常通过
问卷访谈等方式了解用户需求和反馈。然而，这些方法存在一些局
限性，例如调查对象有限、反馈信息不够全面、数据分析效率低下
等。近年来，随着社交媒体和在线评论的普及，越来越多的用户开
始在网上留下对博物馆服务的评论文本。这些评论文本中蕴含着大
量的游客服务体验和情感偏好，成为了研究游客满意度及影响因素
的重要途径。
因此，本文聚焦于部分典型历史博物馆，采集游客针对这些博
物馆的在线评价，提取博物馆服务方面属性词，并聚类归纳属性类
别，抽取属性级语句。然后采用 BERT 模型，对博物馆评论服务方
面的词句进行细粒度的情感分析，挖掘用户需求和偏好，为博物馆
管理者制定更有针对性的服务策略和推广策略提供指导，进而提升
用户满意度和体验。

Museums are an important cultural and educational institution. They play an important role in carrying forward national cultural genes and promoting exchanges and mutual learning among civilizations. They also provide the public with colorful and interesting visiting and learning experiences. However, the quality of the museum's service and user experience are crucial to attracting and retaining visitors. Therefore, by analyzing visitor feedback and clarifying the aspects in urgent need of improvement in the development of Chinese history museum, it will help museum managers to find the existing problems and space for improvement, and improve the quality of service and user experience of the museum.
However, there are some deficiencies in the current research methods of museum satisfaction in the academic circle. Few researchers analyze user opinions through text mining, especially fine-grained user comments mining that can obtain more detailed evaluation attributes and emotional information. In addition, previous studies usually used questionnaires and
interviews to understand user needs and feedback. However, these methods III have some limitations, such as limited survey objects, incomplete feedback information, and low efficiency of data analysis. In recent years, with the popularity of social media and online reviews, more and more users have begun to leave comments and evaluations of museum services online.
These comments contain a large number of tourists' service experience and emotional preference, which has become an important way to study tourists' satisfaction and its influencing factors.
Therefore, this paper focuses on some typical history museums, collects visitors' online evaluation of them, extracts attribute words of museum services, and summarizes attribute categories by clustering, and extracts attribute level statements. Then, BERT model is adopted to conduct fine-grained emotion analysis on the words and phrases of museum review service, dig out users' needs and preferences, and provide guidance for museum managers to develop more targeted service and promotion strategies, so as to improve user satisfaction and experience.