<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we identify teaching signals and employment factors by designing a college student employment guidance work model. The deep learning model is used to identify the given feature vectors, find the word sequence with the highest probability among them, generate the probability of the corresponding acoustic feature vectors, and model the college students’ employment guidance work model to model and calculate them. The teaching signal feature distribution is used to create the description, and the output probability is adjusted to it. The number of college graduates in 2020 will be 6.3 million, an increase of 190,000 compared to last year, and the initial employment rate is 91.07%. The deep learning model can effectively identify college students’ employment confusion, propose effective countermeasures and improve the employment rate.</p>
</abstract>

In this paper, we identify teaching signals and employment factors by designing a college student employment guidance work model. The deep learning model is used to identify the given feature vectors, find the word sequence with the highest probability among them, generate the probability of the corresponding acoustic feature vectors, and model the college students’ employment guidance work model to model and calculate them. The teaching signal feature distribution is used to create the description, and the output probability is adjusted to it. The number of college graduates in 2020 will be 6.3 million, an increase of 190,000 compared to last year, and the initial employment rate is 91.07%. The deep learning model can effectively identify college students’ employment confusion, propose effective countermeasures and improve the employment rate.

Confusion and Countermeasures of College Students’ Career Guidance Work Based on Deep Learning Models

Applied Mathematics and Nonlinear Sciences

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

In this paper, we identify teaching signals and employment factors by designing a college student employment guidance work model. The deep learning model is...

Confusion and Countermeasures of College Students’ Career Guidance Work Based on Deep Learning Models

Online veröffentlicht: 13. Nov. 2023

Seitenbereich: -

Eingereicht: 22. Dez. 2022

Akzeptiert: 24. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01115

Schlüsselwörter
Deep learning models, Feature vectors, College employment, Instructional signals, Output probability

© 2023 Liya Ji, published by Sciendo

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

Confusion and Countermeasures of College Students’ Career Guidance Work Based on Deep Learning Models

Online veröffentlicht: 13. Nov. 2023

Seitenbereich: -

Eingereicht: 22. Dez. 2022

Akzeptiert: 24. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01115

SchlüsselwörterDeep learning models, Feature vectors, College employment, Instructional signals, Output probability

© 2023 Liya Ji, published by Sciendo

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

Schlüsselwörter
Deep learning models, Feature vectors, College employment, Instructional signals, Output probability