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Path of career planning and employment strategy based on deep learning in the information age

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  • Yichi Zhang

Abstract

With the improvement of education level and the expansion of higher education, more students can have the opportunities to obtain better education, and the pressure of employment competition is also increasing. How to improve students’ employment competitiveness, comprehensive quality and the ability to explore paths for career planning and employment strategies has become a common concern in today’s society. Under the background of today’s informatization, the paths of career planning and employment strategies are becoming more and more informatized. The support of Internet is essential for obtaining more employment information. As a representative product of the information age, deep learning provides people with a better path. This paper conducts an in-depth study of the career planning and employment strategy paths based on deep learning in the information age. Research has shown that in the current information age, deep learning through career planning and employment strategy paths can help students solve the main problems they face in career planning education and better meet the needs of today’s society. Career awareness increased by 35% and self-improvement by 15%. This indicated that in the information age, career planning and employment strategies based on deep learning are a way to conform to the trend of the times, which can better help college students improve their understanding, promote employment, and promote self-development.This study combines quantitative and qualitative methods, collects data through questionnaires, and uses deep learning model for analysis. Control group and experimental group were set up to evaluate the effect of career planning education. Descriptive statistics and correlation analysis were used to ensure the accuracy and reliability of the results.

Suggested Citation

  • Yichi Zhang, 2024. "Path of career planning and employment strategy based on deep learning in the information age," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-23, October.
    • Handle: RePEc:plo:pone00:0308654
    DOI: 10.1371/journal.pone.0308654
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    References listed on IDEAS

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    1. Cristiano Felaco & Andrea Zammitti & Jenny Marcionetti & Anna Parola, 2023. "Career Choices, Representation of Work and Future Planning: A Qualitative Investigation with Italian University Students," Societies, MDPI, vol. 13(10), pages 1-11, October.
    2. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
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