This paper uses feature fusion methods to classify the characteristics of dress culture, focusing on the application scope of pixel-level, feature-level, and decision-level. A system for recognizing dress culture classification is constructed by combining machine vision technology, and the support vector machine algorithm is used to solve the nonlinear mapping problem in two-dimensional space. The range of values of support vectors is established by using decision functions to construct a linear hyperplane. The results show that integrating the color system of costume culture and clothing design can lead to new design paths. 70% of the clothing design works have natural color matching, and 60% have color matching that can alter body shape. The research in this paper provides a new reference path for clothing design innovation.