Abstract
INTRODUCTION: "Improving the employment rate of college students" directly affects the stability of the country and society and the healthy development of the industry market. The traditional graduate employment rate model only predicts the future employment rate based on changes in historical emp
Highlights
In recent years, with the expansion of college enrolment, higher education has gradually transformed into popular education
The results show that this method can effectively improve the accuracy of graduate employment rate prediction
The research of artificial intelligence is applied to various fields, and the prediction of employment rate in the field of intelligent education management is the focus of the research
Summary
With the expansion of college enrolment, higher education has gradually transformed into popular education. As the national production and living environment has been affected by the new crown epidemic in recent years, many small and medium-sized enterprises are unable to maintain it. Under this situation: How to predict the relationship between the employment of college students and the trend of national development strategies, so that colleges and universities can adjust their employment strategies and guide college students in a timely manner correct employment values and guide college students on the right path to employment. Predicting the employment rate of college students is an important research aspect of graduate employment quality evaluation. With the continuous development of deep learning, the research on employment rate prediction has become diverse: for example, Xi et al have used neural networks to dynamically adjust parameters and successfully predicted the employment direction of parts
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