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<p>Under the wave of digitization of big data information, the development of artificial intelligence technology continues to deepen the information technology revolution, which has a profound impact on human society and will also revolutionize the employment of different industries and groups. In this paper, we study the evaluation of employment effects of universities based on the neural network Jiangsu artificial intelligence. Since a neural network model needs to be built for training and there is a certain requirement for sample size, Jiangsu AI is selected for the study on the evaluation of employment effects in colleges and universities. The results show that the Dense Net model (C2) has better overall performance than the Vgg Net model, with weight and efficiency values of 89 and 88%, respectively, under the index of the educational level of college employment. Analyzing the impact of AI on college employment and unemployment in Jiangsu for the eight years from 2015 to 2022, it is concluded that with the development of time, the employment rate of college graduates reaches 87%, and the unemployment rate is 13% in 2022. This study has a guiding value for the employment of college graduates, and colleges and universities should focus on the future talent demand, guide students to cope with the possible impact of AI technology actively, cultivate students to acquire core competencies and literacy that AI does not easily replace, and promote high-quality employment of college students.</p>
</abstract>

Under the wave of digitization of big data information, the development of artificial intelligence technology continues to deepen the information technology revolution, which has a profound impact on human society and will also revolutionize the employment of different industries and groups. In this paper, we study the evaluation of employment effects of universities based on the neural network Jiangsu artificial intelligence. Since a neural network model needs to be built for training and there is a certain requirement for sample size, Jiangsu AI is selected for the study on the evaluation of employment effects in colleges and universities. The results show that the Dense Net model (C2) has better overall performance than the Vgg Net model, with weight and efficiency values of 89 and 88%, respectively, under the index of the educational level of college employment. Analyzing the impact of AI on college employment and unemployment in Jiangsu for the eight years from 2015 to 2022, it is concluded that with the development of time, the employment rate of college graduates reaches 87%, and the unemployment rate is 13% in 2022. This study has a guiding value for the employment of college graduates, and colleges and universities should focus on the future talent demand, guide students to cope with the possible impact of AI technology actively, cultivate students to acquire core competencies and literacy that AI does not easily replace, and promote high-quality employment of college students.

Research on the evaluation of the employment effect of Jiangsu artificial intelligence based on neural network on colleges and universities

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Under the wave of digitization of big data information, the development of artificial intelligence technology continues to deepen the information technology...

Research on the evaluation of the employment effect of Jiangsu artificial intelligence based on neural network on colleges and universities

Data publikacji: 07 cze 2023

Zakres stron: -

Otrzymano: 20 cze 2022

Przyjęty: 13 gru 2022

DOI: https://doi.org/10.2478/amns.2023.1.00369

Słowa kluczowe
Artificial intelligence, Neural network, Vgg Net model, Dense Net model, College employment

© 2023 Fujin Hao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the evaluation of the employment effect of Jiangsu artificial intelligence based on neural network on colleges and universities

Data publikacji: 07 cze 2023

Zakres stron: -

Otrzymano: 20 cze 2022

Przyjęty: 13 gru 2022

DOI: https://doi.org/10.2478/amns.2023.1.00369

Słowa kluczoweArtificial intelligence, Neural network, Vgg Net model, Dense Net model, College employment

© 2023 Fujin Hao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Artificial intelligence, Neural network, Vgg Net model, Dense Net model, College employment