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

<p>Aiming at the mental health problems of poor college students, this study proposes an expression recognition method based on the optimization of PCA algorithm and applies it to the mental health test framework. By comparing the differences between poor college students and norms, and using multiple linear regression and typical correlation analysis to explore the impact of educational management paths. The study also addressed the effect of different types of colleges and universities on the level of management of academic support for poor students, and found that 985 colleges and universities significantly outperformed the norm in this area, i.e. <italic>P</italic> > |<italic>t</italic>| = 0.031. This provides valuable insights into improving educational and psychological support for poor college students.</p>
</abstract>

Aiming at the mental health problems of poor college students, this study proposes an expression recognition method based on the optimization of PCA algorithm and applies it to the mental health test framework. By comparing the differences between poor college students and norms, and using multiple linear regression and typical correlation analysis to explore the impact of educational management paths. The study also addressed the effect of different types of colleges and universities on the level of management of academic support for poor students, and found that 985 colleges and universities significantly outperformed the norm in this area, i.e. P > |t| = 0.031. This provides valuable insights into improving educational and psychological support for poor college students.

Optimization of Psychological Characteristics and Education Management Path of Poor College Students in Colleges and Universities Based on Improved PCA

Applied Mathematics and Nonlinear Sciences

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

Aiming at the mental health problems of poor college students, this study proposes an expression recognition method based on the optimization of PCA algorithm...

Optimization of Psychological Characteristics and Education Management Path of Poor College Students in Colleges and Universities Based on Improved PCA

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 08 ene 2024

Aceptado: 17 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0412

Palabras clave
PCA algorithm, Multiple linear regression, Typical correlation, Expression feature recognition, Educational management

© 2024 Zhengrong Ding et al., published by Sciendo

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

Optimization of Psychological Characteristics and Education Management Path of Poor College Students in Colleges and Universities Based on Improved PCA

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 08 ene 2024

Aceptado: 17 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0412

Palabras clavePCA algorithm, Multiple linear regression, Typical correlation, Expression feature recognition, Educational management

© 2024 Zhengrong Ding et al., published by Sciendo

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

Palabras clave
PCA algorithm, Multiple linear regression, Typical correlation, Expression feature recognition, Educational management