[目的/意义]大语言模型的迅速发展改变了传统的人机交互方式，探究主动心理健康场景下大学生群体人智会话交互行为特征有助于理解该情境下用户行为的复杂性和多样性。[方法/过程]通过移动经验取样法进行数据收集，共招募32名被试进行为期两周的人智会话交互实验。借助结构化的日记单进行纵向数据追踪，并通过成熟的心理学量表进行实验前后心理健康状态观测。采用开放编码和会话分析相结合的方法对实验样本数据进行处理和分析。[结果/结论]从用户提问特征、AI回复特征和AI可解释性特征三个方面梳理了人智会话交互的内容特征；从会话结构特征和“提问—回答”行为特征两个方面梳理了人智会话交互行为特征，并对心理健康维度的特征进行了梳理；提出诊断咨询式交互、情感互动式交互和认知协同式交互三种人智会话交互方式。

[Purpose/significance] The rapid advancement of large language models（LLMs）has transformed tradition⁃al human-computer interaction. Investigating the characteristics of human-AI conversational interactions among uni⁃versity students in proactive mental health contexts can enhance our understanding of the complexity and diversity of user behaviors in such scenarios. [Method/process] Data were collected using the mobile experience sampling method (mESM). A total of 32 subjects were recruited for a two-week experiment on human-AI conversational interactions.Longitudinal data tracking was implemented through structured diary logs, while participants′ mental health status was assessed before and after the experiment using validated psychological scales. The experimental data were processed and analyzed through a combined approach of open coding and conversation analysis. [Result/conclusion] The study systematically examines the content characteristics of human-AI conversational interactions through three dimensions:user query features, AI response features, and AI interpretability features. It further analyzes behavioral characteristics through conversational structure features and "query-response" behavioral patterns, while also organizing features re⁃lated to mental health dimensions. Additionally, the study proposes three distinct modes of human-AI interaction: diag⁃nostic-consultative interaction, affective-interactive engagement, and cognitive-collaborative coordination.