Construction of Business English Subject System Based on Data Mining Algorithm

• Published in: 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE) pp. 441 - 445
• Main Author: Bowen, Deng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.09.2021
• Subjects: Business English; Classification algorithms; Data mining; Discipline system construction; Prediction algorithms; Predictive models; Systematics; Training
• DOI: 10.1109/ICISCAE52414.2021.9590650

Discipline construction, as a comprehensive and long-term work in universities, is the foundation and foundation for comprehensively improving the quality of personnel training and the academic and overall level of universities. From the perspective of higher education, discipline refers to a certain scientific field or a branch of science, which has a relatively systematic, relatively complete and independent theoretical system. This paper is the realization of business English discipline system construction based on data mining algorithm. On the basis of highlighting the basic concepts of data mining and the operation of association rules mining, the improved algorithm is put forward, which is superior and effective. The construction method of business English discipline system based on weighted Naive Bayes model calculates the comprehensive weight according to the discipline evaluation attributes and mutual information of different weights, constructs a business English discipline classifier, and realizes an automatic business English discipline classification prediction system, which provides a basic basis for the comprehensive evaluation of business English discipline and lays a foundation for the formulation of a new round of discipline development strategy.