信息记录行为在搜索任务的完成过程中，特别是学习相关的搜索情境中，通常被视为信息使用活动重要的外在表征之一。文章开展了一项用户实验，利用查询片段内的搜索活动来预测用户在搜索过程中是否以及何时发生信息记录行为。结果表明：结合用户在内容页面停留时间和活动转移特征变量，预测准确率达到86.9%。而隐马尔可夫模型（HMM）能够有效地对信息记录行为开展实时预测，特别是在用户的首次查询片段内，准确率更高，为76.65%。研究结果为基于搜索活动对用户的信息记录行为开展实时监测提供了有效的方法，有助于支持信息记录行为和搜索任务完成的搜索系统功能及工具的设计与优化。

Note-taking behavior is often regarded as one of the important external representations of information use activities in the completion of search tasks, especially learning-related search tasks. In this study, we conducted a user experiment to investigate whether and when users' note-taking behavior occurs through the search activities in each query segment. The results indicate that combining users' dwell time on content pages and Activity Transition (AT) features resulted in prediction accuracy of 86.9%. Furthermore, this study also found that a Hidden Markov Models (HMMs) was able to identify note-taking behavior in real time with good performance, especially in the first query segments in each search session, with the accuracy of 76.65%. These findings provide the effective approaches for real-time monitoring of users' note-taking behavior based on search activities, and contribute to the design of search systems that better support note-taking behavior and task completion during search.