Affiliations: [a] School of Art and Design, Huaihua University, Huaihua, China
| [b] School of Landscape Architecture, Huaihua University, Huaihua, China
Correspondence:
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Corresponding author. Qiu Daping, School of landscape architecture, Huaihua University, Huaihua, China. E-mail: 18774711125@139.com.
Abstract: The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.
Keywords: Neural network, image features, ancient architecture, decorative arts, system
