A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era

Abstract

This paper adopts a deep learning approach to analyze and study the mechanism of quantitative enhancement of college students’ employment and entrepreneurial abilities in the context of the digital era. The deep learning connotation is predetermined as five abilities, which are metacognitive ability, active communication and cooperation ability, deep processing ability, creative practice ability, and learning empathy experience, and, based on this, the deep learning questionnaire is designed, and it is reclassified by exploratory factor analysis to reduce the dimensionality, and the specific indicators and scientific connotation dimensions of the deep learning questionnaire are determined; and, through the deep learning of each dimension, the problems of deep learning of college students are examined and in-depth analysis is conducted, and the inner relationship and correlation among the dimensions of deep learning of college students are derived through correlation analysis. The success of innovation and entrepreneurship depends on the innovation and entrepreneurial ability of college students, and the formation of the ability influenced various factors. Therefore, not only is studying the influencing factors of college students’ innovation and entrepreneurship ability in line with the requirements of the times and social development, but also it can solve real problems. This thesis adopts a combination of two methods, qualitative research and quantitative research, to study the influencing factors of college students’ innovation and entrepreneurship ability and tries to ensure the scientificity, accuracy, and comprehensiveness of the conclusion. In this paper, we analyzed the requirements of the employment prediction system for graduating secondary school students, carried out the software framework and database design of the employment analysis and prediction system for secondary school students, and designed the system modularly based on the analysis results. By applying the proposed deep feedforward neural network prediction model to the prediction system, a software system applicable to the employment prediction and guidance of secondary school students is implemented.

Prediction of Employment Index for College Students by Deep Neural Network

“…Especially since 2008, influenced by the international financial crisis, the employment situation has been very serious in our country, the employment of university graduates pressure, according to university students' employment problem, in the new economic situation, to research and explore new methods [15]. erefore, actively promoting the full and harmonious employment of college graduates is an important measure taken by the government to solve the livelihood problems of the people, and it is also a unshirkable responsibility [16].…”

A Systematic Review on the Employability Prediction Model for the Management Students

“…Meng, (2021). [32] International These score are used to find the domains that are based on the subjects. The main purpose was to develop a web application that will help the students to find employable domains based on their scores and skills.…”

A Multiperspective Intervention Path Study of Employment Expectancy in the Era of Big Data

“…In recent years, although many scholars have studied topics such as prediction and planning of college students' career development direction based on data mining and artificial intelligence [15,16] and proposed some research methods there are still shortcomings. For example, in the current study, a large proportion of students' potential graduation destinations are derived from questionnaires, and career counseling teachers, then, plan the corresponding career development directions for students based on their graduation destination choices.…”