An Analysis of Employment Satisfaction of Business English Majors under the background of Belt and Road Initiative

Abstract

The specialty setting and employment guidance in colleges and universities are usually worked out based on empiricism. Data mining technology is rarely used to guide it. In recent years, under the background of Belt and Road Initiative, many schools have offered foreign the majors related to foreign trade English. What is the setting of these majors and the employment satisfaction of graduates? Both have an impact on social and economic development. Access to this information in a timely manner becomes the key. In this paper, the rough set theory, a data mining technology, is used to solve this problem. First of all, this paper collects the employment statistics of foreign trade English graduates from Guangxi Talent International College. Secondly, based on the rough set theory, the quantitative measurement method of college students' employment satisfaction research is put forward, and the intelligent data analysis model based on rough set is established. The experimental results show that the new method is effective and can provide some references for school employment guidance and the training of foreign trade English majors. Finally, the resolution matrix of rough set is used to reduce the attributes of the evaluation data and reduce the unnecessary evaluation indexes. The experimental results show that the evaluation method after the statute is more efficient.