The text discusses the mechanism of network security behavior and public opinion monitoring for college students based on artificial intelligence technology to address the increasingly severe challenges of cyber security threats in modern society. The research first conducts an in-depth analysis of college students’ online behavior patterns and accurately identifies their behavioral characteristics. Based on this, a comprehensive monitoring framework that integrates machine learning and natural language processing technologies has been constructed, capable of real-time capture and updating of network security events and associating information related to college students’ online activities. This framework can effectively identify potential security risks to achieve precise tracking and prediction of public opinion among college students. The research designs intelligent algorithms capable of dynamic monitoring and semantic analysis of massive user-generated content, providing a scientific basis for higher education institutions to conduct cyber security education and formulate relevant policies. The findings of this study offer innovative solutions to enhance the cyber security awareness of college students and regulate their online behavior, providing valuable references for research in the intersection of artificial intelligence and cyber security in both academic and practical fields.