The Classification of Painting Art Styles in Art Teaching Based on Machine Learning from the Perspective of Affective Semantic Analysis

To address the problems of accuracy and efficiency in the classification algorithm of painting art in art teaching, we propose a machine learning-based classification algorithm for painting art in art teaching from the perspective of sentiment semantic analysis. Seven representative art painting styles of western comics, drawing, oil painting, watercolor painting, and domestic branding painting, ink painting and mural painting are selected as the research objects, and the images are pre-processed by denoising and normalization. Subsequently, the style characteristics of the paintings are identified, and the color, chunk, and contour entropies of the pictures are acquired, respectively. These results are then integrated to create a machine learning model of various painting styles. To create the classification model of art painting style, the extracted color, chunk, and contour entropies are merged and trained using a Support Vector Machine (SVM) in art instruction. The approach achieves 94% painting classification accuracy and has the advantages of reduced feature dimensions, fast operation time, and scale invariance.