Analysis of Student Learning Behavior Patterns and Design of Early Warning System Based on Clustering Algorithm

This article aims to explore the application of clustering algorithms in analyzing students' learning behavior patterns, and design an efficient learning warning system based on this. Using educational data mining technology, this study analyzed student interaction data on large-scale online learning platforms, adopted multiple clustering algorithms to identify students' learning behavior patterns, and constructed warning mechanisms based on these patterns. Through quantitative and qualitative indicators, the early warning system can monitor students' learning status in real-time, identify potential academic problems early, and provide intervention suggestions. In the experimental evaluation stage, in the test results of running time, due to the addition of grid partitioning and iterative merging operations on the basis of CF and AGA, the average running time of the algorithm slightly increased. Through comparison, it can be clearly found that the average running time of the algorithm in this paper only needs 5.9 seconds, which is significantly lower than the running time of CF(Collaborative Filtering) and AGA(Adaptive Genetic Algorithm), which is 13.8 seconds and 15.7 seconds. Therefore, the algorithm proposed in this paper is feasible and effective, demonstrating its relative advantages. Future research will focus on further enhancing the universality of early warning systems, exploring more complex learning behavior patterns, and integrating more diverse data sources to optimize prediction models.