Example of node weighted degree (N = 93).

Red tourism is a distinctive form of tourism in China. Its network attention serves as a typical indicator to measure the level of promotion and publicity for red tourism, as well as an important reflection of its influence. Understanding the network structure of red tourism is of significant importance for optimizing the spatial pattern of tourism and promoting the development of the tourism industry. Based on this, this study takes the classic red tourism attractions in Shaanxi province, China as an example and constructs a multi-source data network attention evaluation index. Additionally, it employs social network theory to explore the network attention and tourist flow characteristics of the case study area. Research shows that: (1) Overall, the network attention to case-based destinations is relatively low, and there are significant differences in network attention among different attractions. Spatially, the distribution of network attention is uneven. This is manifested by higher network attention to attractions in Yan’an city and lower network attention to attractions in other regions. (2) There are differences in the network attention of different types of attractions. High-level attractions have a higher level of online attention, while low-level attractions have a lower level of network attention. Additionally, archaeological sites tend to receive a higher level of online attention. (3) The network density of tourist flow is low, and the tourism connections between nodes are not closely linked. The linkage between core nodes and edge nodes in tourism is poor. Developed tourism routes only exist in core nodes. (4) Nodes such as Zaoyuan revolution site, Yangjialing revolution site, and Wangjiaping revolution site have a significant influence in the network structure. In addition, the integration and development between red nodes and non-red nodes have been achieved. (5) There is a correlation between network attention and tourist flow, as well as a ‘misplacement’ feature. Based on the characteristics of attractions, they can be divided into four types: bright-star attractions, cash-cow attractions, thin-dog attractions, and question attractions. Based on the above conclusions, this study proposes targeted development recommendations.红色旅游是中国独具特色的旅游形式。其网络关注度是衡量红色旅游推广宣传水平的典型指标，亦是其影响力的重要体现。解析红色旅游的网络结构，对于优化旅游空间格局、推动旅游业发展具有重要意义。基于此，本研究以中国陕西省经典红色旅游景区为研究对象，构建基于多源数据的网络关注度评价指标体系，并采用社会网络理论探究案例区域的网络关注度与游客流特征。研究结果表明：（1）整体而言，案例地的网络关注度整体偏低，不同景区间的网络关注度存在显著差异；空间维度上，网络关注度分布不均衡，表现为延安地区景区的网络关注度较高，其他区域景区的网络关注度相对较低。（2）不同类型景区的网络关注度存在差异：高等级景区的网络关注度更高，低等级景区则相对较低；此外，考古遗址类景区往往获得更高的网络关注度。（3）游客流网络密度较低，节点间的旅游关联度不高；核心节点与边缘节点的旅游联动性较差，成熟旅游线路仅存在于核心节点之间。（4）枣园革命旧址（Zaoyuan Revolutionary Site）、杨家岭革命旧址（Yangjialing Revolutionary Site）与王家坪革命旧址（Wangjiaping Revolutionary Site）等节点在网络结构中具有显著影响力；此外，红色节点与非红色节点已实现融合发展。（5）网络关注度与游客流之间存在相关性，同时呈现出“错位”特征；根据景区特征可将其划分为四类：明星景区（bright-star attractions）、金牛景区（cash-cow attractions）、瘦狗景区（thin-dog attractions）与问题类景区（question attractions）。基于上述研究结论，本研究提出了针对性的发展建议。