Exploring the Role of Chinese Language and Literature in the Transmission of Traditional Culture by Combining the Method of Internet Text Analysis

Bi Zhao
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Abstract

Abstract This paper constructs a Bayesian network text recognition model based on the Bayesian network and explores the role of Chinese language literature in the dissemination of traditional culture by analyzing the embodiment of traditional culture in Chinese language literature network texts. The collection process of Chinese language and literature data in network text is analyzed from the perspective of textual data interaction. The information of node variables in a Bayesian network is used to determine the mutual relationship between Chinese language literature and traditional culture. The degree of interdependence between Chinese literature and traditional culture can be measured by combining mutual information. The results show that the correct rate of text recognition of the Bayesian text recognition model decreases slightly when the training samples are (100-300), but the correct rate always stays around 0.85, thus reflecting the effectiveness of the network recognition model in this paper. Chinese language literature has a certain role in the dissemination of traditional culture, which proves that Chinese language literature, as a carrier of traditional culture, can improve the dissemination speed of traditional culture. This study focuses on the integration of Chinese literature and traditional communication to improve a new vision.
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结合网络文本分析方法探讨中国语言文学在传统文化传承中的作用
摘要本文构建了基于贝叶斯网络的贝叶斯网络文本识别模型,通过分析传统文化在汉语言文学网络文本中的体现,探讨汉语言文学在传统文化传播中的作用。从文本数据交互的角度分析了网络文本中汉语言文学数据的收集过程。利用贝叶斯网络中节点变量的信息来确定中国语言文学与传统文化的相互关系。中国文学与传统文化的相互依存程度可以通过相互信息的结合来衡量。结果表明,当训练样本为(100-300)时,贝叶斯文本识别模型的文本识别正确率略有下降,但正确率始终保持在0.85左右,反映了本文网络识别模型的有效性。汉语言文学对传统文化的传播具有一定的作用,这证明了汉语言文学作为传统文化的载体,可以提高传统文化的传播速度。本研究着眼于中国文学与传统传播的融合,以提高新的视野。
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来源期刊
Applied Mathematics and Nonlinear Sciences
Applied Mathematics and Nonlinear Sciences Engineering-Engineering (miscellaneous)
CiteScore
2.90
自引率
25.80%
发文量
203
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