Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022)

Chinese Language and Literature Classics Reading under Big Data Network Media

Authors
Jie Cai1, 1, *
1School of Shandong Polytechnic College, Shandong Jining, China
*Corresponding author. Email: caijie0011@163.com
Corresponding Author
Jie Cai
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-058-9_143How to use a DOI?
Keywords
Chinese Language and Literature; Classic Reading; Online Media; Big Data
ABSTRACT

In the big data network media environment, people have more and more ways to obtain information and become more and more convenient. As a ladder of human progress, books should attract everyone's attention, especially the reading of Chinese classics is of great significance to improve Chinese language and cultural literacy. The purpose of this article is to study the reading of Chinese language and literature classics (hereinafter referred to as classics) under big data network media. This study uses interviews and questionnaires to investigate the status quo of classic reading, and analyzes the necessity and urgency of using big data network media technology in classic reading. Through the research and analysis of relevant literature, this paper puts forward the needs and influencing factors of classic reading in the big data network media environment, and provides suggestions on multiple levels to improve the ability to read classic names in the big data network media environment. Starting from the context of the current era of big data network media, this article explores the real situation of classics in the era of big data network media from the perspective of communication, trying to analyze the environment in which classics are formed, and explore the environment of the new media era. Missing, which in turn provides a new perspective for the study of classics. Through research, it is found that among the reading channels, the number of people who tend to read classics through mobile phones is the largest, reaching more than 30%. Computers and tablets and other terminals have the lowest proportion of reading. It can be found that small and flexible mobile devices are more popular among the masses. It is concluded that the study of classic reading under big data network media has certain practical significance.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022)
Series
Advances in Computer Science Research
Publication Date
27 December 2022
ISBN
10.2991/978-94-6463-058-9_143
ISSN
2352-538X
DOI
10.2991/978-94-6463-058-9_143How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Jie Cai
PY  - 2022
DA  - 2022/12/27
TI  - Chinese Language and Literature Classics Reading under Big Data Network Media
BT  - Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022)
PB  - Atlantis Press
SP  - 917
EP  - 922
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-058-9_143
DO  - 10.2991/978-94-6463-058-9_143
ID  - Cai2022
ER  -