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<p>This paper firstly organizes and summarizes the random forest model algorithm in artificial intelligence and secondly uses the XGBoost algorithm with the classification regression tree as the base classifier to explore the relationship between digital financial inclusion, innovative factor allocation and high-quality economic development. Major databases and relevant websites are used to establish the evaluation index system, and the index homogenization is done. The economic high-quality development score is measured using the principal component analysis next. Finally, the principal component scores are weighted and summed according to the proportion of the variance contribution ratio of each principal component to the cumulative variance contribution ratio of the extracted principal components. After calculating the score, it was found that the innovation indicator, which had risen year after year between 2014 and 2018, decreased significantly from 2019 onwards, with the score falling from 2.21 to 1.48, a decrease of 33%. The growth rate of digital financial inclusion declined from 0.93 and finally maintained at 0.45 to 0.47. The economic development index first grew from -11.88 to 2.41, increasing by 3 to 5 each year, and then the growth rate was maintained at about 66%, growing to 6.58 in 2022.</p>
</abstract>

This paper firstly organizes and summarizes the random forest model algorithm in artificial intelligence and secondly uses the XGBoost algorithm with the classification regression tree as the base classifier to explore the relationship between digital financial inclusion, innovative factor allocation and high-quality economic development. Major databases and relevant websites are used to establish the evaluation index system, and the index homogenization is done. The economic high-quality development score is measured using the principal component analysis next. Finally, the principal component scores are weighted and summed according to the proportion of the variance contribution ratio of each principal component to the cumulative variance contribution ratio of the extracted principal components. After calculating the score, it was found that the innovation indicator, which had risen year after year between 2014 and 2018, decreased significantly from 2019 onwards, with the score falling from 2.21 to 1.48, a decrease of 33%. The growth rate of digital financial inclusion declined from 0.93 and finally maintained at 0.45 to 0.47. The economic development index first grew from -11.88 to 2.41, increasing by 3 to 5 each year, and then the growth rate was maintained at about 66%, growing to 6.58 in 2022.

Research on Digital Inclusive Finance, Innovative Factor Allocation and High Quality Economic Development Based on Artificial Intelligence

School of Economics and Management of Tongren University, Rural Revitalization Research Center of Tongren University

Applied Mathematics and Nonlinear Sciences

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

This paper firstly organizes and summarizes the random forest model algorithm in artificial intelligence and secondly uses the XGBoost algorithm with the...

Research on Digital Inclusive Finance, Innovative Factor Allocation and High Quality Economic Development Based on Artificial Intelligence

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 15 déc. 2023

Accepté: 24 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0094

Mots clés
Artificial intelligence, Random forest, XGboost, Digital financial inclusion, Principal component analysis

© 2024 Xiaoye Shang, published by Sciendo

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

Research on Digital Inclusive Finance, Innovative Factor Allocation and High Quality Economic Development Based on Artificial Intelligence

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 15 déc. 2023

Accepté: 24 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0094

Mots clésArtificial intelligence, Random forest, XGboost, Digital financial inclusion, Principal component analysis

© 2024 Xiaoye Shang, published by Sciendo

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

Mots clés
Artificial intelligence, Random forest, XGboost, Digital financial inclusion, Principal component analysis