In today’s society, college students’ moral development is crucial for maintaining social stability and development, making it an urgent issue to address. To more effectively provide valuable ideological and moral development information, we adopt K-Means clustering technology to group two sets of similar users based on their preferences, enabling a more precise provision of valuable ideological and moral development 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 ideological and political education resources is created. Subsequently, the K-Means clustering algorithm is utilized to create a targeted recommendation process based on the given objective function, achieving effective ideological and political education resource recommendations. Through experiments, we find that this approach matches the content of ideological and moral courses well and ensures that students are more interested and have achieved good results.