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

<p>Exploring the innovative teaching methods of international Chinese language education in colleges and universities under the new media environment is to transmit better and develop Chinese culture. This paper constructs an international Chinese language education platform using graph convolutional neural network, RNN algorithm, and long and short-term memory neural network combined with big data technology under artificial intelligence analysis technology and gives that the platform consists of an infrastructure layer, data resource layer, application service layer, and user business layer. The plurality and interactivity indexes are analyzed for the constructed international Chinese education platform. Regarding plurality, A, B, C, and D ratings account for 40.82%, 35.4%, 14.71%, and 9.07%, respectively. Regarding interactivity, the percentage of overall user evaluation data above grade C is 93.4%. This shows that building an international Chinese language education platform can effectively enhance the diversified development of Chinese language education and meet the learning needs of diverse people. The international Chinese education platform uses strong interactivity to achieve communication between users and realize the development of Chinese culture transmission.</p>
</abstract>

Exploring the innovative teaching methods of international Chinese language education in colleges and universities under the new media environment is to transmit better and develop Chinese culture. This paper constructs an international Chinese language education platform using graph convolutional neural network, RNN algorithm, and long and short-term memory neural network combined with big data technology under artificial intelligence analysis technology and gives that the platform consists of an infrastructure layer, data resource layer, application service layer, and user business layer. The plurality and interactivity indexes are analyzed for the constructed international Chinese education platform. Regarding plurality, A, B, C, and D ratings account for 40.82%, 35.4%, 14.71%, and 9.07%, respectively. Regarding interactivity, the percentage of overall user evaluation data above grade C is 93.4%. This shows that building an international Chinese language education platform can effectively enhance the diversified development of Chinese language education and meet the learning needs of diverse people. The international Chinese education platform uses strong interactivity to achieve communication between users and realize the development of Chinese culture transmission.

Innovation of teaching methods of international Chinese language education in universities based on artificial intelligence analysis technology

Applied Mathematics and Nonlinear Sciences

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

Exploring the innovative teaching methods of international Chinese language education in colleges and universities under the new media environment is to...

Innovation of teaching methods of international Chinese language education in universities based on artificial intelligence analysis technology

Pubblicato online: 16 set 2023

Pagine: -

Ricevuto: 02 nov 2022

Accettato: 27 mar 2023

DOI: https://doi.org/10.2478/amns.2023.2.00336

Parole chiave
Graph convolutional neural network, RNN algorithm, Long and short-term memory neural network, Interactivity indexes

© 2023 Xiangli Zhang, published by Sciendo

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

Innovation of teaching methods of international Chinese language education in universities based on artificial intelligence analysis technology

Pubblicato online: 16 set 2023

Pagine: -

Ricevuto: 02 nov 2022

Accettato: 27 mar 2023

DOI: https://doi.org/10.2478/amns.2023.2.00336

Parole chiaveGraph convolutional neural network, RNN algorithm, Long and short-term memory neural network, Interactivity indexes

© 2023 Xiangli Zhang, published by Sciendo

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

Parole chiave
Graph convolutional neural network, RNN algorithm, Long and short-term memory neural network, Interactivity indexes