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Development of an Artificial Intelligence-based Employment Guidance System for College Students--Taking Urban Railway Transportation-Related Majors as an Example

Authors: Junwen, Liu; Weihao, Ni; Yancheng, Lu; Tianyi, Yang;

Development of an Artificial Intelligence-based Employment Guidance System for College Students--Taking Urban Railway Transportation-Related Majors as an Example

Abstract

Abstract: The wide application of artificial intelligence technology in various fields provides new possibilities for workplace informatization and intelligence. At present, the problems of employment difficulties and bottlenecks of college students have become the main bottlenecks restricting the employment of graduates, so the development of college students' employment guidance system based on artificial intelligence technology is of great significance. This study aims to take urban rail transit related majors as an example, through analyzing the current situation of professional employment, designing and developing a college students' employment guidance system based on artificial intelligence technology to provide more intelligent and efficient career planning and employment guidance services for students of this major. The thesis firstly introduces the application of artificial intelligence technology in employment guidance, and specifically describes the way of using artificial intelligence to realize positive career planning and employment guidance. Then, through the analysis of the employment status quo of urban rail transit related majors, we understand the employment situation of the majors, and then design the employment guidance system for college students based on artificial intelligence. Finally, the performance of the system is evaluated through experiments as a way to verify the feasibility and practicality of the system. The experimental results show that the college students' employment guidance system based on artificial intelligence technology designed in this thesis can effectively provide accurate career planning and employment guidance services for students majoring in urban rail transit related majors, and has high practicality and feasibility. Keywords: artificial intelligence; college student career guidance; urban rail transit; major matching; data analysis. Title: Development of an Artificial Intelligence-based Employment Guidance System for College Students--Taking Urban Railway Transportation-Related Majors as an Example Author: Liu junwen, Ni weihao, Lu yancheng, Yang tianyi International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 11, Issue 3, July 2023 - September 2023 Page No: 31-41 Research Publish Journals Website: www.researchpublish.com Published Date: 13-July-2023 DOI: https://doi.org/10.5281/zenodo.8143116 Paper Download Link (Source) https://www.researchpublish.com/papers/development-of-an-artificial-intelligence-based-employment-guidance-system-for-college-students--taking-urban-railway-transportation-related-majors-as-an-example

International Journal of Computer Science and Information Technology Research, ISSN 2348-1196 (print), ISSN 2348-120X (online), Research Publish Journals, Website: www.researchpublish.com

Keywords

data analysis, https://www.researchpublish.com/papers/development-of-an-artificial-intelligence-based-employment-guidance-system-for-college-students--taking-urban-railway-transportation-related-majors-as-an-example, artificial intelligence, urban rail transit, college student career guidance, major matching

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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