<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, data mining is proposed to study the evolution of China’s modern history by addressing the problem of incomplete content of the evolution process. The data mining technique mainly preprocesses the data set of Chinese modern history by logistic regression algorithm, and its purpose is to detect the accuracy of the data so as to provide accurate and high-quality data for the data mining process. The process of visualization using information related to the evolution of modern Chinese history and the influence of modern Chinese historical events is applied to the visualization analysis, and the final influence of the evolutionary development of modern Chinese history is obtained and saved to the database by weighting and summing the influence factors of modern Chinese historical figures. The logistic regression algorithm uses modern historical persons and things as input data, and the weights of modern historical persons and things are the predictions carried out by classification. The results show that the highest accuracy is 0.67 when the threshold value is set to 1. The logistic classification model predicts better for the case of weight type 2 of modern Chinese history people and weight type 6 of modern history things. This study makes a certain contribution to the study of modern history so that the study of modern history can gradually move toward completeness and objectivity.</p>
</abstract>

In this paper, data mining is proposed to study the evolution of China’s modern history by addressing the problem of incomplete content of the evolution process. The data mining technique mainly preprocesses the data set of Chinese modern history by logistic regression algorithm, and its purpose is to detect the accuracy of the data so as to provide accurate and high-quality data for the data mining process. The process of visualization using information related to the evolution of modern Chinese history and the influence of modern Chinese historical events is applied to the visualization analysis, and the final influence of the evolutionary development of modern Chinese history is obtained and saved to the database by weighting and summing the influence factors of modern Chinese historical figures. The logistic regression algorithm uses modern historical persons and things as input data, and the weights of modern historical persons and things are the predictions carried out by classification. The results show that the highest accuracy is 0.67 when the threshold value is set to 1. The logistic classification model predicts better for the case of weight type 2 of modern Chinese history people and weight type 6 of modern history things. This study makes a certain contribution to the study of modern history so that the study of modern history can gradually move toward completeness and objectivity.

Analysis of the evolution of modern Chinese history based on data mining

Beijing Technology and Business University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Analysis of the evolution of modern Chinese history based on data mining"}

In this paper, data mining is proposed to study the evolution of China’s modern history by addressing the problem of incomplete content of the evolution...

Analysis of the evolution of modern Chinese history based on data mining

Online veröffentlicht: 02. Okt. 2023

Seitenbereich: -

Eingereicht: 03. Nov. 2022

Akzeptiert: 12. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00428

Schlüsselwörter
Data mining, Logistic regression algorithm, Modern Chinese History, Visual analysis

© 2023 Yue Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of the evolution of modern Chinese history based on data mining

Online veröffentlicht: 02. Okt. 2023

Seitenbereich: -

Eingereicht: 03. Nov. 2022

Akzeptiert: 12. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00428

SchlüsselwörterData mining, Logistic regression algorithm, Modern Chinese History, Visual analysis

© 2023 Yue Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Data mining, Logistic regression algorithm, Modern Chinese History, Visual analysis