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

<p>The improvement of a new rural financial legal system can protect the legitimate rights and interests of rural farmers. In this paper, after preprocessing the text of the legal system, the Word2vec word vector model is used to represent the legal text features, select the legal text features using information gain, extract the legal text features based on the LDA topic model, and complete the construction of the legal text feature engineering. On this basis, the random forest algorithm based on the decision tree is improved by text similarity, and the text classifier is constructed to realize the mining of legal text content. After verifying the performance of legal system text mining, the relevant high-frequency words of the legal system are extracted to explore the path of improving the legal system of new rural cooperative finance. The results show that among the 18 high-frequency words extracted, finance (298 times), report (242 times), management (221 times), and statistics (221 times), all of them are more than 200 times, focusing on the financial development as the top priority for the improvement of the legal system of new rural cooperative finance. Based on the research of this paper, it is important and positive significance to improve the legal system of new rural cooperative finance, which is the key to realizing rural revitalization.</p>
</abstract>

The improvement of a new rural financial legal system can protect the legitimate rights and interests of rural farmers. In this paper, after preprocessing the text of the legal system, the Word2vec word vector model is used to represent the legal text features, select the legal text features using information gain, extract the legal text features based on the LDA topic model, and complete the construction of the legal text feature engineering. On this basis, the random forest algorithm based on the decision tree is improved by text similarity, and the text classifier is constructed to realize the mining of legal text content. After verifying the performance of legal system text mining, the relevant high-frequency words of the legal system are extracted to explore the path of improving the legal system of new rural cooperative finance. The results show that among the 18 high-frequency words extracted, finance (298 times), report (242 times), management (221 times), and statistics (221 times), all of them are more than 200 times, focusing on the financial development as the top priority for the improvement of the legal system of new rural cooperative finance. Based on the research of this paper, it is important and positive significance to improve the legal system of new rural cooperative finance, which is the key to realizing rural revitalization.

Research on the Improvement Path of New Rural Cooperative Financial Legal System Based on Decision Tree Modeling

Applied Mathematics and Nonlinear Sciences

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

The improvement of a new rural financial legal system can protect the legitimate rights and interests of rural farmers. In this paper, after preprocessing the...

Research on the Improvement Path of New Rural Cooperative Financial Legal System Based on Decision Tree Modeling

Published Online: Jan 31, 2024

Page range: -

Received: Dec 22, 2023

Accepted: Jan 01, 2024

DOI: https://doi.org/10.2478/amns-2024-0273

Keywords
Word2vec word vector, Information gain, LDA topic model, Random forest algorithm, Financial legal system

© 2024 Hesheng Xu, published by Sciendo

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

Research on the Improvement Path of New Rural Cooperative Financial Legal System Based on Decision Tree Modeling

Published Online: Jan 31, 2024

Page range: -

Received: Dec 22, 2023

Accepted: Jan 01, 2024

DOI: https://doi.org/10.2478/amns-2024-0273

KeywordsWord2vec word vector, Information gain, LDA topic model, Random forest algorithm, Financial legal system

© 2024 Hesheng Xu, published by Sciendo

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

Keywords
Word2vec word vector, Information gain, LDA topic model, Random forest algorithm, Financial legal system