An Improved BP Neural Network-based Quality Evaluation Model for Chinese International Education Teaching Courses

Abstract

For learners of Chinese as a second language, it is difficult to learn Chinese in the target language environment. With the large-scale applications of intelligent technology in Chinese international education, Chinese teaching is moving towards digitalization, networking and informatization, and computer technology is promoting the development of Chinese international education in a more convenient way. In order to effectively improve the teaching quality of Chinese international education, a scientific and reasonable evaluation system of course teaching quality needs to be formulated. This paper addresses the shortcomings of the traditional course teaching quality evaluation methods, introduces BP neural network to establish the teaching evaluation base model, and sets the corresponding course evaluation indicatores as the input quantity. Considering the problems of local optimum and low convergence efficiency of BP neural network, an improved BP neural network is proposed to optimize the initial weights and thresholds of BP neural network using the beetle antennae search algorithm, so as to obtain the improved teaching quality evaluation model of Chinese international education courses. Then, the performance test of the model is conducted in this paper. The test results show that the proposed teaching quality evaluation model can accurately evaluate the teaching quality of the curriculum and help improve the teaching courses of Chinese international education, which is an innovative attempt to combine computer technology with Chinese international education. Finally, this paper puts forward corresponding suggestions for the combination of Chinese international education and computer technology, in order to promote the further development of Chinese international education.