【摘要】 在进一步推进文化和旅游深度融合的背景下，如何对文旅融合水平进行评测成为一个研究问题。文章从需求侧视角出发，构建文化氛围视角的文旅融合水平测度框架，并引入大语言模型辅助内容分析方法，对文化和旅游部设立的文化产业和旅游产业融合示范区内代表性景区的文旅融合水平进行实证测算，系统评估其文旅融合水平与类型特征，以验证测度框架的理论合理性与方法有效性。结果表明：1）文化氛围测度是剖析和衡量需求侧文旅融合效果的有效理论路径，基于大语言模型辅助内容分析方法在文旅融合水平评测中具有可靠性；2）文化氛围测度能有效区分景区文旅融合表现类型，具体划分为高文化氛围型、高情感体验型、高身体体验型和低文化氛围型，这些类型呈现出清晰的结构性和层次性差异；3）供给侧评测强调的资源禀赋是文化氛围形成与提升的基础，但并不必然转化为游客的文化氛围感知。有效的文化叙事与价值传播才是增强游客情感共鸣与精神认同、体现文旅融合水平的关键。该研究为文旅融合评估研究与实践提供了新的理论路径与实践方法。更多还原

【Abstract】 With the deepening integration of culture and tourism, evaluating integration performance through the lens of tourist experience has become a key research issue. From a demand-side perspective,this study constructs a measurement framework for cultural and tourism integration based on the concept of cultural atmosphere and introduces a large language model(LLM)-assisted content analysis method. Using large-scale online reviews from 29 representative scenic spots within China’s newly established National Cultural and Tourism Integration Demonstration Zones as empirical cases, the study quantitatively measures their integration levels along three experiential dimensions—physical, emotional, and spiritual—identifies integration types, and verifies the theoretical validity and methodological robustness of the proposed framework. The results show that: 1) Cultural atmosphere measurement provides an effective theoretical approach for analyzing and evaluating demand-side integration outcomes, and the LLM-assisted content analysis method demonstrates strong reliability in assessing integration levels. The overall integration performance of these scenic spots exhibits significant dispersion: the mean composite score is moderate,with a wide gap between the highest-and lowest-scoring scenic spots. High-scoring scenic spots typically display balanced profiles across physical, emotional, and spiritual dimensions, whereas lower-scoring ones tend to overemphasize physical sensations while lagging in emotional resonance and spiritual identification.Verification with tourist ratings and platform scores confirms that the derived indicators track perceived quality more closely than a generic sentiment baseline. 2) The cultural atmosphere measurement effectively distinguishes four integration types with distinct structural characteristics among the scenic spots—high cultural atmosphere, high emotional experience, high physical experience, and low cultural atmosphere.High cultural atmosphere types achieve balanced and high-level performance by combining effective narrative design with value communication, thereby converting cultural display into emotional resonance and identity recognition. High emotional experience types pair strong storytelling with deep content development, producing closely aligned emotional and spiritual responses grounded in rich heritage resources. High physical experience types excel in sensory participation but often fall short of elevating sensations into enduring meanings due to weaker narrative transformation. Low cultural atmosphere types underperform across all dimensions. 3) Resource endowments emphasized by supply-side assessments are foundational yet insufficient to produce a strong cultural atmosphere on the demand side. Differences in heritage value levels do not directly correspond to tourists’ perceived atmosphere or its subdimensions.Scenic spots with prominent historical and cultural value can still register muted atmosphere when interpretation is insufficient or communication channels are ineffective; conversely, scenic spots with modest endowments can cultivate salient cultural atmosphere through innovative storytelling, localized displays, and clear value transmission. Theoretically, this study substantiates cultural atmosphere as a demand-side measurement framework for culture-tourism integration, demonstrates its feasibility and applicability at the micro level, and helps reorient integration research from supply-side resource inventories to demand-side experience-oriented logics. Methodologically, it establishes an LLM-driven, review-based evaluation framework that converts large-scale user-generated content into multidimensional, interpretable indicators. At the same time, it provides strategic guidance for public authorities and destination managers in promoting the deep integration and coordinated development of culture and tourism.更多还原