探讨政府开放数据平台中用户隐私政策阅读意愿的影响因素，分析其多因素组态路径，揭示用户阅读隐私政策意愿的内在机制，能够为提升用户隐私意识、优化隐私政策设计提供理论支持与实践指导。文章基于隐私计算理论（扩展）与UTAUT2模型，首先通过问卷收集数据，以结构方程模型（SEM）分析隐私担忧、隐私保护意愿、绩效期望、社会影响等因素对用户隐私政策阅读意愿的直接影响；其次，结合模糊集定性比较分析法（fsQCA）识别多因素组合路径，以揭示用户隐私政策阅读意愿的多重驱动逻辑。研究发现，影响用户隐私政策阅读意愿的关键因素包括隐私担忧、隐私保护意愿、绩效期望、努力期望、享乐动机、便利条件和习惯，信任与社会影响结果不显著。最终识别出三种主要组态路径，包括隐私敏感型、社会信任型和便利依赖型，并从提升用户隐私意识、优化隐私政策设计、增强用户对隐私保护的信任等方面提出了建议。

Explores the factors influencing users' willingness to read privacy policies on government open data platforms, analyzes the multi-factor configuration pathways, and uncovers the underlying mechanisms of users' willingness to engage with privacy policies. The findings aim to provide theoretical support and practical guidance for enhancing users' privacy awareness and optimizing privacy policy design. Based on the extended privacy calculus theory and the UTAUT2 model, this study first collected data through surveys and used structural equation modeling (SEM) to analyze the direct effects of factors such as privacy concerns, privacy protection intentions, performance expectancy, and social influence on users' willingness to read privacy policies. Then, fuzzy set qualitative comparative analysis (fsQCA) was applied to identify multi-factor configuration pathways and reveal the multiple driving logics behind users' privacy policy reading intentions. The study found that the key factors influencing users' willingness to read privacy policies include privacy concerns, privacy protection intentions, performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, and habits. Trust and social influence were found to have insignificant effects. Three main configuration pathways were ultimately identified: privacy-sensitivity, social trust, and convenience-dependence. Based on these findings, recommendations were made to simplify the language of privacy policies, improve the user interface design of privacy policies, and enhance users' trust in privacy protection.