Interoperability Analysis between Traditional Chinese Sculpture and Painting Modeling from the Perspective of Big Data Management

Abstract

Chinese traditional sculpture and painting have strong interoperability in terms of patterns, colors, and lines. Chinese sculpture and painting art are traditional Chinese works of art. The art of painting is often the basis for sculptural art. A good sculptural work of art often requires the pattern and color foundation of the painting. Moreover, Chinese traditional sculpture artworks often reflect certain historical information and humanistic spirit. Traditional artificial methods are often difficult to discover the intercommunication between cultural information between Chinese sculpture and painting. For the interoperability between sculpture and painting artworks, artists only rely on professional knowledge and aesthetic ability to discover some interoperability in patterns, colors, and lines, which is insufficient for understanding Chinese sculpture and painting. This study designs a novel hybrid CNN-LSTM method to study the interoperability of Chinese sculptures and paintings in terms of patterns, colors, lines, and cultural information. CNN can extract patterns, colors, and line features of Chinese paintings and sculptures. The cultural characteristics of Chinese sculptures have obvious temporal characteristics, which can be mined by LSTM technology. The research results show that the hybrid CNN-LSTM method has good feasibility and accuracy in studying the interoperability of traditional Chinese sculpture and painting. In terms of average error, the largest error is only 3.03% and this part of the error comes from the prediction of Chinese sculpture and painting cultural information. All other features of traditional sculpture and painting are predicted to be within 3%. For the prediction of color features, the error is only 1.13%. Prediction errors for patterns, colors, and lines are within acceptable limits.