Journal of Intelligent Computing Yubao Shen 16 1 2025 https://doi.org/10.6025/jic/2025/16/1/10-18 https://www.dline.info/jic/fulltext/v16n1/jicv16n1_2.pdf The development of big data technology has driven the pace of teaching innovation and reform. In the information age, education emphasizes personalized and comprehensive development of students more than ever before. This paper combines big data algorithms to construct an intelligent education model for classroom student profiling. The model leverages big data mining algorithms to discover the correlations in student behavior data. Using classification algorithms based on multi-frequency patterns, the model classifies student behavior data and constructs multi-frequency pattern trees for students with different academic performance, reflecting differences in their learning behavior characteristics. Experimental results demonstrate that applying the intelligent education model based on big data algorithms can effectively provide teachers with comprehensive and accurate feedback on student behavior characteristics, helping students understand their learning situations and enabling targeted personalized teaching, significantly improving students’ learning quality and efficiency.