[目的/意义]研究主题所属学科分类是学科交叉测度以及学科交叉主题识别等学科交叉以及跨学科研究的基础，只有确定每个研究主题所属的学科门类，才能判断其是否为学科交叉点。[方法/过程]构建基于大模型的研究主题学科分类框架，在Llama3-8B-Instruct、Qwen2.5-7B-Instruct和DeepSeek-R1-Distill-Qwen-7B三个基座模型基础上，采用“领域自适应预训练+监督微调”双阶段优化策略，利用116192篇学术论文进行领域预训练，以增强模型对科学文献的语义理解能力，以作者自标注关键词表征研究主题，通过人工标注的126919条“研究主题—学科标签”数据集进行监督微调，以优化模型对研究主题的学科分类能力。[结果/结论]虽然大语言模型具有零样本学科分类能力，但是仅仅靠设计Prompt使用大模型的精确率和F1值均低于50%，难以满足实际需要；而构建的研究主题所属学科分类框架精确率达93.61%、F1值达83.09%，显著提升了研究主题所属学科分类效果。

[Purpose/significance] Classifying research topics into their respective disciplines is fundamental for inter⁃disciplinary research, such as measuring interdisciplinary integration and identifying cross-disciplinary themes. Only by determining the disciplinary category of each research topic can we assess whether it represents an interdisciplinary intersection. [Method/process] This study proposes a framework for classifying research topics into disciplines using large language models. Building on base models such as Llama3-8B-Instruct, Qwen2.5-7B-Instruct, and DeepSeek-R1-Distill-Qwen-7B, a two-stage optimization strategy of "domain-adaptive pretraining + supervised fine-tuning" was implemented. A total of 116192 academic papers were used for domain-adaptive pretraining to enhance the model′s semantic understanding of scientific literature. Author keywords were used to represent research topics, and a manual⁃ly annotated dataset of 126919 "research topic-disciplinary label" pairs was employed for supervised fine-tuning to op⁃timize the model′s classification performance. [Result/conclusion] While large language models possess zero-shot dis⁃ciplinary classification capabilities, their precision and F1-scores remain below 50% when relying solely on prompt de⁃sign, which is insufficient for practical applications. In contrast, the proposed framework achieves a precision of 93.61% and an F1-score of 83.09%, significantly improving the accuracy of disciplinary classification for research top⁃ics.