Research on Graduation Destination Prediction Algorithm Based on Students’ Learning Behavior Data

With the popularization of higher education, colleges and universities and educational institutions are increasingly concerned about students' performance and development during the learning process, and hope to improve graduation and employment rates through effective prediction methods. The study first preprocesses the data, then uses the clustering algorithm to study the correlation between the graduation trend of the graduating cohort and the students' competency indicators, and selects the best model from multiple classification algorithms through cross-validation, and predicts students' future graduation destinations with Micro-F1 and Macro-F1 values. The experimental results show that the key points of graduation direction are student's professional ability>student's behavioral regularity in school>student's family economic condition>student's personal interest, and the accurate prediction rate is 70.7%, which is significantly higher than the 44.8% accuracy of random guessing, indicating that the data mining algorithms can more accurately predict the future graduation direction of the students, and it is expected that they provide useful references for the colleges and universities to provide scientific decision-making and personalized counseling for the students. It is expected to provide a useful reference for colleges and universities to provide scientific decision-making and personalized counseling for students.