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

<p>In recent years, the examination of literary creation through the lens of the natural geographic environment has emerged as a focal point in literary studies. This study designates China’s geographic environment as the independent variable and ancient Chinese literary creation as the dependent variable. We employ Pearson’s correlation coefficient to assess the appropriateness of the selected variables. Following this, a linear regression model is constructed using the least squares method to explore the relationship between geographic and environmental differences and ancient literary creation. The findings demonstrate significant positive correlations between the characteristics of topography, climate, and soil and the thematic content of ancient Chinese literature, with significance levels below 0.05. Specifically, the Pearson correlation coefficients are 0.509 for topographic features, 0.366 for climatic features, and 0.418 for soil features. Furthermore, the overall regression model exhibits an adjusted R² value of 0.235 and an F-value of 80.759. This research delineates the regional attributes that impact literary creation, thereby enhancing our understanding of the interplay between geography and literary output in ancient China.</p>
</abstract>

In recent years, the examination of literary creation through the lens of the natural geographic environment has emerged as a focal point in literary studies. This study designates China’s geographic environment as the independent variable and ancient Chinese literary creation as the dependent variable. We employ Pearson’s correlation coefficient to assess the appropriateness of the selected variables. Following this, a linear regression model is constructed using the least squares method to explore the relationship between geographic and environmental differences and ancient literary creation. The findings demonstrate significant positive correlations between the characteristics of topography, climate, and soil and the thematic content of ancient Chinese literature, with significance levels below 0.05. Specifically, the Pearson correlation coefficients are 0.509 for topographic features, 0.366 for climatic features, and 0.418 for soil features. Furthermore, the overall regression model exhibits an adjusted R² value of 0.235 and an F-value of 80.759. This research delineates the regional attributes that impact literary creation, thereby enhancing our understanding of the interplay between geography and literary output in ancient China.

A Study on the Influence of Geographic Differences in China on Ancient Chinese Literary Creation in the Context of Deep Learning

Applied Mathematics and Nonlinear Sciences

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

In recent years, the examination of literary creation through the lens of the natural geographic environment has emerged as a focal point in literary studies....

A Study on the Influence of Geographic Differences in China on Ancient Chinese Literary Creation in the Context of Deep Learning

Online veröffentlicht: 09. Juli 2024

Seitenbereich: -

Eingereicht: 26. Feb. 2024

Akzeptiert: 22. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1817

Schlüsselwörter
Linear regression model, Least squares method, Correlation analysis, Ancient literary creation

© 2024 Shiyi Wang., published by Sciendo

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

A Study on the Influence of Geographic Differences in China on Ancient Chinese Literary Creation in the Context of Deep Learning

Online veröffentlicht: 09. Juli 2024

Seitenbereich: -

Eingereicht: 26. Feb. 2024

Akzeptiert: 22. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1817

SchlüsselwörterLinear regression model, Least squares method, Correlation analysis, Ancient literary creation

© 2024 Shiyi Wang., published by Sciendo

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

Schlüsselwörter
Linear regression model, Least squares method, Correlation analysis, Ancient literary creation