随着信息技术的快速发展，个性化学习资源的推荐系统越来越受到重视。本文研究了基于大模型技术的个性化学习资源推荐方法，旨在通过深度学习和自然语言处理技术，提高学习资源推荐的精确性和个性化水平。本文详细介绍了大模型技术，尤其是Transformer架构及其在教育领域的应用潜力。通过利用现代的预训练语言模型，如GPT和BERT，本研究构建了一个能够理解并处理复杂用户查询的智能推荐系统。
本文的系统设计包括一个动态更新的学习资源库和一个高效的查询处理机制，利用向量化技术加速查询过程，显著提升了系统的响应速度和处理大规模数据的能力。实验结果表明，该推荐系统能够有效地提供与用户需求高度相关的教育资源，优化了用户的学习体验。
本研究不仅扩展了个性化推荐系统的应用范围，还探讨了大模型技术在教育技术领域的实际应用，为未来相关研究提供了理论基础和实践指导。随着技术的进一步发展，期待本系统能在多样化和跨文化的教育环境中发挥更大的作用。

With the rapid development of information technology, personalized learning resource recommendation systems have gained increasing attention. In order to enhance the precision and personalization of learning resource recommendations, we investigate personalized learning resource recommendation methods based on large language model, deep learning and natural language processing techniques. Initially, we introduce large language model, particularly the Transformer architecture and its potential applications in the educational field. By integrating modern pre-trained language models such as GPT and BERT, we construct an intelligent recommendation system capable of understanding and processing complex user queries.
Our system design includes a dynamically updated learning resource database and an efficient query processing algorithm. We utilize vectorization technology to accelerate the query process, significantly enhancing the system's response speed. Experimental results demonstrate that the recommendation system effectively provides educational resources highly relevant to user needs.
This study not only expands the application scope of personalized recommendation systems but also explores the practical application of large model technology in the field of educational technology. As technology further develops, this system is expected to play a more significant role in diverse and cross-cultural educational environments.