Abstract

Online education is the core field in China now, and many online education institutions have reached thousands or even thousands of teachers. A large amount of information has been gathered in the corresponding personnel management system modules and talent databases of the institution itself, such as personnel student status query management and performance evaluation analysis and management. How to reuse these data to transform existing data management into usable knowledge has become a problem that organizations should not underestimate. The purpose of this paper is to study the theoretical knowledge related to data mining algorithms, put forward the process and classification of data mining, and focus on the mining process and common methods of data classification rules. Experimental data shows that 31% of parents chose online education in 2019, compared with only 14% in 2017. Among them, the natural lack of offline education resources in third- and fourth-tier cities online education makes up for the lack of offline resources. The results show that third- and fourth-tier cities will become the driving force for the development of online education in the future.

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