<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The combination of Chinese language and literature and information technology in the context of big data is an important way to cultivate all-around Chinese language talents. This paper constructs a teaching platform for modern Chinese language and literature based on a big data information fusion algorithm and first establishes a differential equation to express the development capability of Chinese language informatization. The quantitative recursive analysis is used to perform data fusion and teaching ability assessment for the platform, and the data classification is used to collect big data and quantitatively analyze the teaching ability assessment value of the platform, from which the utilization rate of teaching resource distribution is obtained. Finally, according to the feature vector of platform teaching ability assessment, clustering, and integration of index parameters are carried out to realize student talent cultivation and platform informatization development. The results show that the experimental and control groups were set up to teach through this platform for one semester, and the analysis of the students’ comprehensive final exam scores yielded a total average score of 86.8606 for the experimental class, which was significantly greater than the total average score of 81.4418 for the control class, thus indicating that the education system combining modern Chinese language and literature with informatization can enhance students’ talent ability.</p>
</abstract>

The combination of Chinese language and literature and information technology in the context of big data is an important way to cultivate all-around Chinese language talents. This paper constructs a teaching platform for modern Chinese language and literature based on a big data information fusion algorithm and first establishes a differential equation to express the development capability of Chinese language informatization. The quantitative recursive analysis is used to perform data fusion and teaching ability assessment for the platform, and the data classification is used to collect big data and quantitatively analyze the teaching ability assessment value of the platform, from which the utilization rate of teaching resource distribution is obtained. Finally, according to the feature vector of platform teaching ability assessment, clustering, and integration of index parameters are carried out to realize student talent cultivation and platform informatization development. The results show that the experimental and control groups were set up to teach through this platform for one semester, and the analysis of the students’ comprehensive final exam scores yielded a total average score of 86.8606 for the experimental class, which was significantly greater than the total average score of 81.4418 for the control class, thus indicating that the education system combining modern Chinese language and literature with informatization can enhance students’ talent ability.

Information Development and Student Talent Cultivation of Modern Chinese Language and Literature in the Context of Big Data

Applied Mathematics and Nonlinear Sciences

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

The combination of Chinese language and literature and information technology in the context of big data is an important way to cultivate all-around Chinese...

Information Development and Student Talent Cultivation of Modern Chinese Language and Literature in the Context of Big Data

Pubblicato online: 09 ago 2023

Pagine: -

Ricevuto: 27 set 2022

Accettato: 17 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00157

Parole chiave
Big data information fusion algorithm, Quantitative recursion, Eigenvectors, Differential equations, Quantitative analysis

© 2023 Kehong Tan et al., published by Sciendo

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

Information Development and Student Talent Cultivation of Modern Chinese Language and Literature in the Context of Big Data

Pubblicato online: 09 ago 2023

Pagine: -

Ricevuto: 27 set 2022

Accettato: 17 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00157

Parole chiaveBig data information fusion algorithm, Quantitative recursion, Eigenvectors, Differential equations, Quantitative analysis

© 2023 Kehong Tan et al., published by Sciendo

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

Parole chiave
Big data information fusion algorithm, Quantitative recursion, Eigenvectors, Differential equations, Quantitative analysis