Information Security Education Journal Jiang Jing 12 2 2025 https://doi.org/10.6025/isej/2025/12/2/49-56 https://www.dline.info/isej/fulltext/v12n2/isejv12n2_2.pdf The paper proposes an intelligent secure education system tailored for college students using personalized recommendation technology. Recognizing the growing security challenges students face ranging from cybersecurity and traffic safety to mental health the system aims to enhance security awareness through customized learning content. It integrates user management, resource management, recommendation algorithms, and user interfaces into a cohesive architecture. Central to the system is a matrix factorization algorithm enhanced with techniques like multi head self attention and feed forward neural networks to analyze user behavior and recommend relevant educational resources. The system preprocesses diverse resource types (text, images, video) and extracts features using methods such as word embeddings and convolutional neural networks. Experimental evaluation compares an experimental group using the personalized system against a control group using traditional methods. Metrics like recommendation accuracy, recall, and user satisfaction are analyzed, with results indicating improved learning effectiveness and engagement. The figures in the paper illustrate the impact of parameters, such as K, on recommendation performance and compare algorithm accuracy using metrics such as MAE. The study concludes that personalized recommendation systems significantly enhance secure education by aligning content with individual student needs, and it calls for further refinement through user feedback and advanced algorithms to address evolving security education demands.