Exploring the Integration and Value of Chinese Traditional Elements and Modern Clothing Design under Big Data Technology

Abstract

Abstract Under the rich cultural background, clothing design should effectively integrate traditional culture to provide people with better quality clothing products and meet the demands of the development of clothing culture. This paper analyzes the classification and application value of traditional elements and explores the expression strategies and application steps of conventional elements in clothing design. Then, we use the VGG network as the basis and combine the semantic extraction network and style migration network to extract the styling features of traditional elements, introduce the OTSU algorithm to extract the pattern features of traditional cultural elements, and combine the improved K-Means clustering algorithm to extract the main color of conventional elements. The practice of clothing design was conducted using the Republican style as a source of inspiration, and the effectiveness of the mentioned methods and the application of clothing design were evaluated and analyzed. It was found that the color purity extracted by the improved K-Means clustering algorithm was about 2.5 times higher than the result of the octree method, and the testers’ ratings of the pattern and novelty of the clothing design reached 3.81 and 4.29, respectively. The technique allows for the effective integration of traditional elements extracted based on the method into modern clothing design, promoting more diversity in the visual expression of contemporary clothing design.