Construction of Intelligent Analysis Method for Online Learning of Preschool Education Based on Clustering Algorithm

Aiming at the problem that resource matching and resource content of online learning of preschool education are the good and bad are intermingled, an intelligent analysis model for online learning of preschool education based on optimized K-means clustering algorithm is proposed to evaluate and analyze the quality of online education resources. Where, analytic hierarchy process (AHP) and weight-based optimized initial centroid selection were introduced to improved the clustering algorithm. The results show that the clustering accuracy of the optimized clustering algorithm has been greatly improved. Compared with the original K-means algorithm and density-based K-means algorithm, the accuracy of the optimized clustering algorithm is 7.47% and 4.16% higher, and the clustering effect is better. Experiments show that the intelligent analysis model for online learning of preschool education based on the optimal clustering algorithm can effectively evaluate the quality and situation of learning resources, and has a high promotion value in the online learning of preschool education according to learner behavior statistics and resource defect analysis.