Optimization of Data Mining and Analysis System for Chinese Language Teaching Based on Convolutional Neural Network.

Xi Chen

doi:10.1155/2021/1148954

Abstract

Chinese language is also an important way to understand Chinese culture and an important carrier to inherit and carry forward Chinese traditional culture. Chinese language teaching is an important way to inherit and develop Chinese language. Therefore, in the era of big data, data mining and analysis of Chinese language teaching can effectively sum up experience and draw lessons, so as to improve the quality of Chinese language teaching and promote Chinese language culture. Text clustering technology can analyze and process the text information data and divide the text information data with the same characteristics into the same category. Based on big data, combined with convolutional neural network and K-means algorithm, this paper proposes a text clustering method based on convolutional neural network (CNN), constructs a Chinese language teaching data mining analysis system, and optimizes it so that the system can better mine Chinese character data in Chinese language teaching data in depth and comprehensively. The results show that the optimized k-means algorithm needs 683 iterations to achieve the target accuracy. The average K-measure value of the optimized system is 0.770, which is higher than that of the original system. The results also show that K-means algorithm can significantly improve the clustering effect, optimize the data mining analysis system of Chinese language teaching, and deeply mine the Chinese data in Chinese language teaching, so as to improve the quality of Chinese language teaching.

Highlights

Chinese language is the language with the longest history and the largest number of users in the world, so Chinese language teaching has been valued by people from all walks of life [1]
Is paper creatively combines convolutional neural network, feedback neural clustering algorithm, and K-means clustering algorithm and proposes a CK-TC algorithm. e algorithm can learn the semantic relationship between Chinese words and sentences on the basis of large-scale corpus, convert the text information into original vectors, and express words and sentences in the form of word vectors
After the system is optimized by using feedback neural algorithm and cyclic neural network, the F-measure value of the system is significantly improved, which shows that the feedback neural algorithm and cyclic neural network have obvious optimization effect on the system and can effectively improve the performance of the system

Summary

Introduction

Chinese language is the language with the longest history and the largest number of users in the world, so Chinese language teaching has been valued by people from all walks of life [1]. Erefore, combining convolutional neural network and K-means clustering algorithm, this paper proposes a K-means algorithm and constructs and optimizes the Chinese language teaching data mining analysis system based on this algorithm, so as to realize the deep mining of Chinese language teaching data and improve the quality of Chinese language teaching. Based on the public data of Iowa, Saeed and Zeebaree used the improved k-prototype clustering algorithm combined with BP neural network to build the prediction model of the recidivism rate after the criminals were released from prison. Convolutional neural network can train and learn the characteristics of these original vectors, construct text vectors, cluster these text vectors by using the optimized k-means algorithm, and construct and optimize the Chinese teaching data mining and analysis system

Data Mining and Analysis System for Chinese Language Teaching

Findings

Discussion

Conclusion