Journal of Information Technology Review Jiang Jing 16 2 2025 https://doi.org/10.6025/jitr/2025/16/2/63-70 https://www.dline.info/jitr/fulltext/v16n2/jitrv16n2_3.pdf In today’s society, the moral development of college students is crucial for maintaining social stability and growth, making it an urgent issue to address. To provide more practical information on moral development, we utilize K-Means clustering technology to group users based on their preferences, enabling the provision of more precise and valuable information. Firstly, the data is classified through a collaborative filtering algorithm to ensure data comparability and determine user preferences. Based on this, an effective model for recommending educational resources is created. Subsequently, the K-Means clustering algorithm is employed to develop a targeted recommendation process based on the specified objective function, yielding an effective recommendation of educational resources. Through experiments, we have found that this approach not only aligns well with the content of moral courses but also ensures that students are more engaged and achieve good results in reality.