With the rapid development of artificial intelligence technology, more and more universities are exploring student behavior analysis. This study uses artificial intelligence methods and tools to conduct in-depth analysis of the behavioral characteristics of university students, identify key factors that affect academic performance and mental health, and first use data mining and machine learning techniques to analyze multidimensional data such as students’ learning habits, social interactions, and psychological states, and evaluate the impact of these factors on academic performance and campus adaptability. Based on the analysis results, targeted management strategies were proposed, including personalized learning support, mental health interventions, and social skills training. Verify the effectiveness of artificial intelligence technology in student behavior monitoring and management through empirical research on case universities. At the same time, it also discussed the challenges and solutions that may be faced during the implementation process, providing practical and feasible suggestions for university managers.