Research on Student Behavior Pattern Recognition and Personalized Management Based on Intelligent Algorithm

This study focuses on student behavior pattern recognition and personalized management based on intelligent algorithm, aiming at realizing personalized education with the opportunity of the rapid development of educational informatization and promoting students’ all-round development. In this study, an intelligent algorithm based on integrated learning is adopted, and three models, decision tree, random forest and Gradient Boosting Machine (GBM), are combined to deeply analyze the collected online learning records of students. In the data preprocessing stage, data quality and algorithm processing efficiency are improved by data cleaning, missing value processing, single heat coding and Z-score standardization. After algorithm design and optimization, this study successfully realized the accurate identification of students’ behavior patterns, and applied it to personalized management practice. The research results show that the intelligent algorithm based on integrated learning has achieved remarkable results in identifying students’ behavior patterns, and the recognition accuracy rate is as high as ${9 5. 8 \%}$, which is obviously superior to other similar research methods. In addition, this study also reveals the relationship between different students’ behavior patterns and academic performance, which provides strong data support for personalized management. The research on student behavior pattern recognition and personalized management based on intelligent algorithm not only improves the accuracy and stability of student behavior pattern recognition, but also provides an effective means for personalized management in the field of education.