Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 8, Issue 2


Published
on

August 7, 2023


Pages


DOI

Article

Innovative Strategies for the Development of International Chinese Language Education Based on Deep Learning Models

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Authors

Tiantian Wang Affiliation:
School of International Education, Yiwu Industrial & Commercial College, Yiwu, Zhejiang, 322000, China


Abstract

The development of international Chinese language education greatly impacts the building of a global Chinese cultural environment. This paper constructs a model structure based on a convolutional neural network with a deep learning model and explains it in detail, and uses it to analyze the current development situation of international Chinese language education. The paper also presents the performance evaluation of the convolutional neural network model and discusses the problems in the development of international Chinese language education and the innovative development direction of online teaching. From the characteristics of innovative online teaching materials, rich media, mobility, interactivity, personalization, timeliness, and open sharing become the main melodies of the development of international Chinese language education, and the five-year average values of each characteristic are 53.59%, 51.17%, 49.77%, 47.84%, 45.94%, and 42.19%, respectively. The convolutional neural network model based on deep learning can effectively analyze the problems of international Chinese language education and the direction of innovation, providing an effective technology to help the development of Chinese culture in China.


Keywords

Deep learning model, Convolutional neural network, Activation function, International Chinese language education, 68M01


Citation

Wang, T. (2023). Innovative strategies for the development of international chinese language education based on deep learning models. Applied Mathematics and Nonlinear Sciences, 8(2). https://doi.org/10.2478/amns.2023.2.00138

Published by: Engineering Journals

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