Evaluation and Stratification for Chinese International Education Quality with Deep Learning Model.
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Abstract
In the process of human communication, language learning and communication play a fundamental, leading, broad, and long-lasting role. It serves as a link and a bridge between countries and peoples, allowing for greater understanding and camaraderie. The significance of Chinese in international commercial and cultural exchanges has been more obvious in the current era, as China’s comprehensive strength continues to improve. Its cultural value and practical value have been continuously improved, the international community has an increasing demand for learning Chinese, and the cause of international Chinese language education has developed rapidly. China must strengthen the international dissemination of Chinese, so that the world can better understand and accept China, so that China can better integrate into the world. In this context, how to evaluate and stratify the quality of Chinese international education has become an important research topic. Relying on the hot deep learning technology in recent years, this work designs a neural network for evaluating the quality of international Chinese education. The content of this work is as follows: aiming at the serious defect of the current mainstream feature classification network that only uses the top-level features extracted by a single convolution layer to classify, which leads to the loss of classification accuracy. A multiscale feature pyramid fusion network is built in this paper, starting with the working mechanism of a convolutional neural network. It is capable of fully extracting and combining the representations of the network’s shallow and deep outputs, based on first- and second-order characteristics of global and local discriminative region information. Second, the network structure has the bottleneck layer module and the batch normalization layer module, both of which are made up of varying numbers of 1 × 1 convolution kernels.Copyright © 2022 Min He.