Over the past few years, the swift advancement of the internet has been accompanied by substantial advancements in related applications. The Internet-based online learning system allows students to disregard the constraints of time and location, facilitating a more convenient learning process. This system’s ability to transcend the limitations of traditional classroom settings aligns well with the contemporary educational need for lifelong learning, while also enhancing the spectrum of educational diversity. As online learning systems become increasingly integrated into society, learners are able to engage in seamless digital and comprehensive learning through various application platforms. Traditional online learning systems focus more on integrating educational resources, but do not provide personalized resource recommendations for different students. When simply porting traditional web recommendation algorithms to online learning systems, the effectiveness of personalized recommendation learning is poor due to the lack of consideration for the transfer of student interests over time. This article mainly focuses on the intelligent recommendation algorithm for online learning resources based on recommendation systems. It first summarizes relevant research, proposes relevant application methods, and discusses the results of the research.