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In order to improve the coloration efficiency of fashion design, an automatic color matching mechanism based on adaptive color clustering of image scenes is proposed for clothing patterns. Taking images of Sung porcelain as an example, 300 porcelain images from six different kilns were collected as testing samples. The porcelain area of each sample image was detected by image segmentation and denoising. The bipartite K-means clustering algorithm was utilized to adaptively extract the main colors of each sample. Also, the secondary clustering was carried out to obtain the main color values, proportions and co-occurrence ratios of each kiln’s image. A dynamic color matching mechanism integrating the number of color clusters, co-occurrence ratio, and structure characteristics of the target pattern, was designed and automatic color parsing and matching software was developed. The experiment results show that the color matching control parameters, source images, pattern shape, and other factors affect the main color selection sequence and the final matching effect. The time consumed for single sample color extraction and pattern automatic color matching are all less than 1 s, which can quickly realize the pattern color migration based on image scenes and further provide auxiliary decision-making for clothing pattern color design.