【目的/意义】 研究基于人工智能技术构建重大科技基础设施科学数据管理与推荐框架的方法，重点探讨融合知识图谱与大语言模型的个性化推荐系统架构，以提升科学数据的整合与智能推荐能力。 【方法/过程】 提出一种基于人工智能技术的重大科技基础设施科学数据管理与推荐框架，引入知识图谱、链路预测和大语言模型（Large Language Models，LLMs， LLMs）技术，提出面向科研全流程的个性化数据推荐系统架构。 【结果/结论】 系统架构包括数据采集、知识抽取、语义推理、个性化推荐等模块。数据层负责多源异构数据的整理和整合，分析层通过图神经网络与大语言模型进行深度分析，应用层则为科研人员提供精准的推荐服务。所提出的框架有助于提高数据的利用率，促进科技创新能力的提升，提高多源异构数据的整合与智能推荐能力。

[Purpose/Significance] Large-scale research infrastructures represent a symbol of national scientific competitiveness, and the scientific data they generate play a crucial role in scientific research, technological advancement, and economic development. However, traditional data management methods face challenges such as data redundancy, difficulties in sharing, and repetitive experiments, urgently requiring empowerment through next-generation artificial intelligence technologies. This paper aims to study a method for constructing an AI-enabled scientific data management and recommendation framework for large-scale research infrastructures, focusing on exploring a personalized recommendation system architecture that integrates knowledge graphs and large language models to enhance the integration and intelligent recommendation capabilities of scientific data. [Method/Process] This paper proposed an artificial intelligence-based framework for scientific data management and recommendation for large-scale research infrastructures. It introduced technologies such as knowledge graphs, link prediction, and large language models (LLMs), proposing a personalized data recommendation system architecture oriented towards the entire research process. [Result/Conclusion] The system architecture includes modules for data collection, knowledge extraction, semantic reasoning, and personalized recommendation. The data layer is responsible for the organization and integration of multi-source heterogeneous data, the analysis layer performs in-depth analysis using graph neural networks and large language models, and the application layer provides accurate recommendation services to researchers. The framework proposed in this paper contributes to improving data utilization, promoting scientific and technological innovation capabilities, and enhancing the integration and intelligent recommendation of multi-source heterogeneous data.