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

<p>Based on the background of education informatics, this paper uses natural language processing technology to analyze the teaching effect in the Chinese language and literature. By analyzing the basic methods of processing natural language, the CBOW model is constructed using text vectorization. Combined with the LDA model for Chinese language literature text, a keyword is automatically extracted. GRU is chosen as the master control unit of the information feature loop. The CBOW model is optimized using the gradient descent optimization algorithm, which improves the analysis efficiency of education and teaching. Relevant strategies for improving humanistic quality are proposed by analyzing the humanistic quality of Chinese language and literature majors in colleges and universities using natural language processing technology. The results show that the CBOW model performs better in analyzing the textual features of Chinese language and literature, and its MR value is 0.8007±0.0028 compared with the traditional neural network models such as RNN, CNN, etc. Among the strategies for improving humanistic qualities under the education of Chinese language and literature, the effect of strengthening the dissemination of cultural knowledge is 0.8. This study promotes the education of Chinese language and literature in colleges and universities to a certain degree, which is It is conducive to improving students’ humanistic literacy.</p>
</abstract>

Based on the background of education informatics, this paper uses natural language processing technology to analyze the teaching effect in the Chinese language and literature. By analyzing the basic methods of processing natural language, the CBOW model is constructed using text vectorization. Combined with the LDA model for Chinese language literature text, a keyword is automatically extracted. GRU is chosen as the master control unit of the information feature loop. The CBOW model is optimized using the gradient descent optimization algorithm, which improves the analysis efficiency of education and teaching. Relevant strategies for improving humanistic quality are proposed by analyzing the humanistic quality of Chinese language and literature majors in colleges and universities using natural language processing technology. The results show that the CBOW model performs better in analyzing the textual features of Chinese language and literature, and its MR value is 0.8007±0.0028 compared with the traditional neural network models such as RNN, CNN, etc. Among the strategies for improving humanistic qualities under the education of Chinese language and literature, the effect of strengthening the dissemination of cultural knowledge is 0.8. This study promotes the education of Chinese language and literature in colleges and universities to a certain degree, which is It is conducive to improving students’ humanistic literacy.

Chinese Language and Literature Education and Humanistic Literacy Enhancement in Colleges and Universities under the Background of Education Informatization

Applied Mathematics and Nonlinear Sciences

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

Based on the background of education informatics, this paper uses natural language processing technology to analyze the teaching effect in the Chinese language...

Chinese Language and Literature Education and Humanistic Literacy Enhancement in Colleges and Universities under the Background of Education Informatization

Online veröffentlicht: 29. Nov. 2023

Seitenbereich: -

Eingereicht: 23. Jan. 2023

Akzeptiert: 09. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01282

Schlüsselwörter
Natural language processing, CBOW model, Gradient descent algorithm, Pedagogical analysis, Chinese language and literature

© 2023 Fangfei Li, published by Sciendo

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

Chinese Language and Literature Education and Humanistic Literacy Enhancement in Colleges and Universities under the Background of Education Informatization

Online veröffentlicht: 29. Nov. 2023

Seitenbereich: -

Eingereicht: 23. Jan. 2023

Akzeptiert: 09. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01282

SchlüsselwörterNatural language processing, CBOW model, Gradient descent algorithm, Pedagogical analysis, Chinese language and literature

© 2023 Fangfei Li, published by Sciendo

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

Schlüsselwörter
Natural language processing, CBOW model, Gradient descent algorithm, Pedagogical analysis, Chinese language and literature