[目的/意义] 针对现有档案需求研究多聚焦机构层面、对公众个体化和场景化档案诉求刻画不足的问题，以社交媒体平台用户生成内容为研究对象，构建面向公众档案需求的“识别—分类—预测”分析框架，为档案服务精准供给与动态适配提供方法支撑。[方法/过程] 以知乎平台档案相关问答文本为语料，综合运用BERTopic主题模型、人工核验与大语言模型辅助语义归纳方法，识别公众档案需求主题并完成类别归并；进一步基于各类别月度频次构建时间序列，比较SARIMA与Prophet模型的预测效果，并对未来短期需求变化进行预测。[结果/结论] 研究发现，公众档案需求可归纳为人事档案流转与凭证类需求，机构日常档案管理类需求，监督、合规与问责类档案需求3类，分别对应个人事务办理、机构运行管理和治理监督问责3个层面。时序分析显示，人事档案流转与凭证类需求和机构日常档案管理类需求具有长期基础性和周期性特征，监督、合规与问责类档案需求则具有较强事件驱动性。研究结果表明，社交媒体平台能够有效反映公众真实的档案诉求，语义主题模型与时间序列模型的结合可为档案部门开展需求感知、资源配置和人事档案智能化服务提供参考。

[Purpose/Significance] To address the limitations of existing archival demand studies, which have mainly focused on the institutional level and paid insufficient attention to individualized and scenario-based archival needs among the public, this study takes user-generated content on social media platforms as its research object and constructs an “identification-classification-prediction” analytical framework for public archival demand, providing methodological support for the precise provision and dynamic adaptation of archival services. [Methods/Process] Using archival-related Q&A texts from Zhihu as the corpus, this study integrated the BERTopic topic model, manual verification, and large language model-assisted semantic induction to identify public archival demand topics and consolidated them into categories. Furthermore, monthly frequencies of each category were used to construct time series, the forecasting performance of SARIMA and Prophet models was compared, and short-term future changes in demand were predicted. [Result/Conclusion] The results show that public archival demand can be summarized into three categories: personnel archive circulation and credential-related demand, institutional routine archive management demand, and archive demand related to supervision, compliance, and accountability, corresponding respectively to personal affairs handling, institutional operation and management, and governance supervision and accountability. Time-series analysis indicates that personnel archive circulation and credential-related demand, as well as institutional routine archive management demand, exhibit long-term foundational and periodic characteristics, while archive demand related to supervision, compliance, and accountability is strongly event-driven. The findings suggest that social media platforms can effectively reflect the public’s real archival needs, and that combining semantic topic models with time-series models can provide references for archival departments in demand sensing, resource allocation, and the optimization of intelligent personnel archive services.