Application of Cluster Analysis Algorithm in the Online Intelligent Teaching Art Resource Platform

Abstract

With the explosion of knowledge and the high-speed dissemination of information, people’s desire for knowledge and information is getting stronger and stronger. At the same time, the updating of knowledge and information is going on at an unprecedented speed. The traditional teaching mode is affected by time and space. Its limitations have become more and more prominent; the traditional classroom teaching has been unable to meet the existing teaching needs. At present, there are many methods for the analysis of network user behavior, such as statistical methods, association analysis methods, and clustering algorithms. Among them, clustering algorithms are more widely used in network user behavior analysis, which is closely related to the unsupervised and high efficiency of clustering algorithms. This paper combines the advantages of clustering algorithm in network user behavior analysis and, on the basis of the existing clustering algorithm research, proposes an improved algorithm for the analysis of online intelligent teaching art resources, so as to obtain the law of online behavior of student users in campus network. Provide some help for students’ Internet management and network optimization. Finally, summarize and put forward the concept of intelligent teaching and design and implement an online intelligent teaching art resource platform based on cluster analysis algorithm. Studies have shown that the average number of transactions processed by the platform per second is 65.21, which can well simulate real information query use cases. The transaction processing time of the platform will eventually stabilize between 30 s and meet the performance requirements.