Comparative Study of Convolutional Neural Networks and Traditional Methods in Textile Color Matching

As the textile industry grows rapidly, color matching accuracy becomes more and more critical. Traditional methods have gradually exposed problems such as low efficiency and limited accuracy when dealing with complex textile colors. In this context, convolutional neural network technology comes into being, bringing new ideas for color matching. This paper first explains the research background of the two, points out the core of the problem, and then details the specific content of the research on traditional methods and convolutional neural networks (CNN) in textile color matching, including algorithm principles, data processing procedures, etc. Through a large number of experimental studies, the matching effects of the two in different scenarios are compared and analyzed. The results show that the convolutional neural network has advantages in many aspects. Its color recognition accuracy can reach up to 0.88, and its anti-interference ability is also better than traditional methods, providing a better solution for textile color matching and promoting the intelligent development of the industry.