Chinese Painting Algorithm: A Study of Scene Characterization by Chromatographic Multiple Analysis and Handwriting Co-construction

Abstract

The understanding of scene representation is a deep knowledge service structure strategy arising from the increasing scale of data and the need for complex logic solving. This study proposes a modeling improvement method based on the fusion of complex feature data and exploration behavior trajectory extremes, which effectively utilizes the artistic feature study of the interplay between the unique colorant mixture attachment features of Chinese painting and complex handwriting features as the orientation region, realizes the classification constraint of colorant data through multispectral detection, and characterizes the handwriting as the behavior law, realizes the parametric extraction and then couples the solution encoding to complete the improvement of the algorithm. Since all scenes in Chinese painting are recorded in the bearer medium with handwriting characteristics after mixing Chinese brushes and colorants, the computational model of Chinese painting algorithm proposed in this paper starts from the processing of representation hierarchical structure and painting behavior of various scenes deposited to realize the principle of describing their material deposition goals and information exchange functions. The experimental analysis shows that i. deep knowledge understanding achieves the derivation of sparse feature validity, ii. the coverage calculation obtained by drawing on technological means can vividly describe the implicit characteristics of handwriting behavior, and iii. the improved modeling process has more humanized perceptual habits and enhances the accuracy and robustness of service domain requirements.