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

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.

Keywords

• deep learning models
• feature vectors
• college employment
• instructional signals
• output probability
• 97m20