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Classification and Recognition of Internet Literature Text Based on Data Mining Technology

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Cyber Security Intelligence and Analytics (CSIA 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 123))

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Abstract

With the rapid spread and development of the Internet, Internet text has also become the main source of information and receiving source in human daily life, and how to obtain more valuable information and knowledge from this information has also become an urgent problem to be solved. This article has carried out research on the classification and recognition of Internet literature text based on data mining technology. It understands the relevant theories of Internet literature text classification and recognition on the basis of literature data, and then designs the classification and recognition of Internet literature text based on data mining technology. In order to increase the accuracy of the analysis, the weighting method in classification and recognition was improved, and then tested to prove it. The result showed that the improved weighting method is more accurate in the same dimension than before the improvement, and it is in the four types of weighting. Among the methods, the accuracy is the largest and the classification effectiveness is better.

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Acknowledgements

Natural Science Foundation of Xinjiang Uygur Autonomous Region: 2020D01A34.

Education Reform Fund project of Guangdong Province: CXQX-JY201806.

Feature innovation project of colleges and universities in Guangdong province: 2020KTSCX163.

Feature innovation project of colleges and universities in Guangdong province: 2018KTSCX256.

Guangdong baiyun university key project: 2019BYKYZ02.

Special project in key fields of colleges and universities in Guangdong Province: 2020ZDZX3009.

Author information

Authors and Affiliations

  1. Faculty of Education, Guangdong Baiyun University, Guangzhou, 510450, Guangdong, China

    Rong Xiong

  2. Guangdong Polytechnic of Water Resources and Electric Engineering, Guangzhou, 510635, Guangdong, China

    Zichen Li

  3. Faculty of Megadate and Computing, Guangdong Baiyun University, Guangzhou, 510450, Guangdong, China

    Yongjun Qi

  4. University of Manitoba, Winnipeg, Canada

    Thomas Lavoie

Authors
  1. Rong Xiong
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  2. Zichen Li
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  3. Yongjun Qi
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  4. Thomas Lavoie
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Editor information

Editors and Affiliations

  1. School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, China

    Zheng Xu

  2. Department of Information Systems and Security, College of IT, United Arab Emirates University (UAEU), Abu Dhabi, United Arab Emirates

    Saed Alrabaee

  3. Altair Management Consultants, Madrid, Spain

    Octavio Loyola-González

  4. Department of Info Systems & Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA

    Xiaolu Zhang

  5. Informatics Faculty, Telkom University, Bandung, Indonesia

    Niken Dwi Wahyu Cahyani

  6. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia

    Nurul Hidayah Ab Rahman

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Xiong, R., Li, Z., Qi, Y., Lavoie, T. (2022). Classification and Recognition of Internet Literature Text Based on Data Mining Technology. In: Xu, Z., Alrabaee, S., Loyola-González, O., Zhang, X., Cahyani, N.D.W., Ab Rahman, N.H. (eds) Cyber Security Intelligence and Analytics. CSIA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 123. Springer, Cham. https://doi.org/10.1007/978-3-030-96908-0_81

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