The Data Management of the Aesthetic Value of Hunan Oil Paintings in the Cultural Context Provides Knowledge Support for the Development of Hunan Oil Paintings

Wenqin Zeng; Pichai  Sodbhiban

doi:10.60027/iarj.2026.e293896

Authors

Wenqin Zeng Bangkok Thonburi University, Thailand https://orcid.org/0009-0008-5454-3074
Pichai Sodbhiban Bangkok Thonburi University, Thailand https://orcid.org/0009-0005-1361-4081

DOI:

https://doi.org/10.60027/iarj.2026.e293896

Keywords:

Data Management, Aesthetic Value, Hunan Oil Painting, Cultural Context, Digital Humanities, Knowledge Graph

Abstract

Background and Aims: This research establishes a knowledge framework for the development of Hunan oil painting by analyzing its aesthetic value management within a cultural context. It aims to construct a comprehensive data-driven system that systematically quantifies aesthetic characteristics, maps cultural networks, and supports the sector's creative, academic, and commercial development.

Methodology: The study adopts a mixed-methods design, integrating quantitative computational techniques (including improved ResNet-50 models and knowledge graph construction) with qualitative art historical analysis. The methodology proceeded through three phases: (1) systematic mapping of Hunan oil painting's trajectory and theories; (2) construction of a multidimensional dataset from artworks, archives, and market data (N=1,850 works); and (3) establishment of a technical framework using big data analytics and deep learning.

Results: The results demonstrated: (1) the establishment of an evaluation index system for the aesthetic value of Hunan oil painting, introducing the quantifiable concept of "cultural data density"; (2) the development of the Hunan Oil Painting Digital Asset Management Platform (prototype) and a Visual Yearbook (1949-2025); (3) successful interdisciplinary integration, creating a paradigm combining art, data science, and cultural geography. The ResNet-50 model achieved 91.3% classification accuracy (95% CI: 89.5-93.1%), and a knowledge graph with 37,000 relationships was constructed.

Conclusions: This study provides a replicable, data-driven paradigm for regional art research. By transforming implicit aesthetic values into explicit, manageable knowledge assets through the "cultural context–aesthetic value–data management" model, it offers substantial support for the creative innovation, academic study, and sustainable development of Hunan oil painting.

References

Arnold, D. (2021). Art history: A very short introduction. Oxford University Press.

Chen, Z. (2018). Regional studies of Chinese oil painting. People’s Fine Arts Publishing House.

Duan, J. (2019). Hunan memories in the digital age [Interview]. China Oil Painting.

Elgammal, A., et al. (2017). Can computers define art? IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1362–1377.

Florida, R. (2019). The rise of the creative class. Basic Books.

Getty Research Institute. (2023). Art & Architecture Thesaurus Online. https://www.getty.edu/research/tools/vocabularies/aat/

Huang, T. (2020). Hunan culture and my watercolor oil painting exploration. Art Observation, 5, 45–49.

Hunan Art Museum. (2021). Xiao Xiang lineage: Centennial exhibition album of Hunan oil paintings. Hunan Fine Arts Publishing House.

Hunan Provincial Archives. (2015). Hunan art development historical materials compilation (1949–2010) [Internal materials].

Hunan Provincial Department of Culture and Tourism. (2022). Hunan Province’s 14th Five-Year Plan for art development (Xiang Zhengfa [2022] No. 15).

Li, J., & Wang, H. (2019). Art big data: Theory and application. Tsinghua University Press.

Luo, Z. (2005). Visual reconstruction of rural China—My creative journey. Art Research, 3, 12–18.

Manovich, L. (2020). Cultural analytics. MIT Press.

UNESCO. (2021). Cultural statistics framework. UNESCO Publishing.

Zhang, X. (2022). Analysis of contemporary art schools in China based on knowledge graphs. Computer-Aided Design, 145, 103272. https://doi.org/10.1016/j.cad.2021.103272