Homogenization of textiles and garments, the mismatch between supply and demand, and slow design decisions have long been a bottleneck in the development of a high-quality industry. With the activation of consumer trends such as “oriental aesthetics” and “new fashion”, the regeneration of traditional, national and localized clothing spirit and cultural connotation has opened up new ideas and new ways for consumption growth. Color research based on image analysis technology is helpful to accurately, conveniently, and objectively characterize garment composition forms and color usage patterns and build a bridge between subjective perception and quantitative analysis, thus helping the development and application of intelligent color design for fashion products. In order to clearly explain the color distribution and association rules of She traditional costumes, image analysis techniques were utilized to parse the imagery coloration relationship. Taking the She diaspora in Zhejiang, Jiangxi and Fujian provinces as an example, the coloration of 150 representative costumes images obtained from the field survey were analyzed. Firstly, the selected samples were subjected to image pre-processing operations. Secondly, in the construction stage of the base dataset, if conventional K-means clustering was used, the number of color clusters of each sample was forced to be uniform, which would easily lead to color lifting bias. Therefore, an improved dichotomy K-means adaptive clustering algorithm was used here to adaptively extract the color of each garment. Under HSV color space, the main colors of the sequence samples were extracted. On this basis, the K-means algorithm was used for secondary clustering when regional differences were compared horizontally, and the number of common color categories of She was determined according to relevant literature studies to unify the number of cluster centers and obtain the clothing imagery colors of each region. The improved vector set-based Apriori algorithm was used to resolve the multivariate color matching relationships among the imagery colors of She clothing, and to visually characterize the color patterns and correlations of different regional settings at the same time. Experimental results show that the quantified colors of She clothing in the three regions match the colors used in She clothing recorded in current literature, and the color palette as a whole presents black, red and blue. In the binary color matching relationship, there are some differences in the color matching color groups of She clothing in each region. In Zhejiang province, there are different shades of similar colors interwoven with each other, showing a rich sense of hierarchy; in Jiangxi province, the colors are deep and simple, with matching frequency of black and gray being greater than 50%, while in Fujian province, there are a small amount of red, yellow and other colors embellishing with each other. In the ternary color scheme relationship, the She clothing pairing is richer in Jiangxi province, simpler in Zhejiang province, and as a whole shows black, green, dark yellow and light yellow pairing relationships with each other in Fujian province. The average time cost of color matching rules parsing with the improved algorithm is 0.032 seconds, which can quickly parse the color correlation rules of She costumes coloration, and provides a referenced method of color analysis and regeneration design for other similar traditional costumes. This study analyzes the color relationships of She clothing in Zhejiang, Jiangxi, and Fujian, and concretely represents the color usage patterns and color matching logic of clothing, which helps to realize the digital conservation of She clothing coloration. In addition, the objective and visualized way of characterizing the color mechanism of clothing provides a basis for color assignment for the future application of color activation and regeneration design of She clothing and further provides a methodological reference for the systematic study of similar traditional clothing colors. [ABSTRACT FROM AUTHOR]