以统计分析挖掘主题信息作为生成式大模型的知识输入，可提升学术选题的可解释性。基于人工预选方向，从知网检索文献，经主题分析、特征降维、主题组合挖掘与多维度指标推荐，再由生成式模型生成标题。与直接生成标题相比，该方法产出的选题更具体深入，统计流程与数据可增强选题可解释性。但本研究仍存在不足：分步选题仅在流程层面实现，未融入生成模型；主题组合未结合用户偏好；选题结果未经过专家验证。总体而言，融合统计分析与生成式技术的分步选题，可显著提升学术选题的可靠性与可解释性。

Using statistical analysis to mine topic information as input for generative models improves the interpretability of academic topic selection. According to manual preliminary directions, literature is retrieved from CNKI, processed by topic analysis, feature dimensionality reduction, topic combination mining and multi-dimensional recommendation, and then titles are generated by the model.Compared with direct generation, this method produces more specific and in-depth topics, and the statistical process improves interpretability. However, limitations exist: step-by-step selection is only at the process level, not integrated into the model; user preferences are ignored; and results lack expert verification. In general, step-by-step topic selection combining statistics and generative technology greatly improves reliability and interpretability.