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In the production process of raw liquid colored polyester, there will be color changes from fibers to yarns and from yarns to fabrics. The color changes of yarns and fabrics are often not caused by the color changes of the fibers themselves. There are many factors that can cause color changes, such as yam thickness, twist, twist direction, and fabric structure. At present, the judgment of color changes from fibers to yarns and fabrics by enterprises mainly relies on manual experience, which is subjective and difficult to control. Therefore, accurately predicting the color patterns between fibers, yarns, and fabrics has practical application value for enterprise production. In recent years, many domestic researchers have developed many theoretical models for color prediction in the textile field. Traditional color matching models include the Friele model, Stearns-Noechel model, and Kubelka-Munk model, which have certain limitations. To control the regularity of color parameters L, a, and b in the production process of polyester fibers, yarns, and fabrics dyed with raw liquid, and to improve the accuracy of color prediction between fibers, yarns, and fabrics, this paper took the samples of polyester fibers, yarns, and fabrics dyed with raw liquids produced by enterprises as the research object and proposed a color prediction method based on neural networks, which are intelligent computing methods that simulate biological neural networks in computer network systems. Firstly, a colorimeter was used to obtain the L, a, and b values of 288 sets of color samples, each containing fiber, yarn, and fabric samples of the same color. Then, the samples were divided into three training groups, namely fiber yarn group, yarn fabric group, and fiber fabric group. Simultaneously, the data were input into the network for modeling, with fiber groups as inputs and yarn groups as targets; the yarn group served as the input, and the fabric group served as the target; fibers served as input and fabrics as target. Finally, the performance of the neural network was adjusted based on the average Eus of the network and the color difference between the output and the true color. The training algorithm, number of neurons, and transfer function were adjusted separately. This article expanded the sample data to 1, 000 groups by adding noise, enhancing the network's generalization ability and improving the accuracy of experimental results. The experimental results show that with trainlm as the training algorithm, when the number of neurons is 100 and the transfer functions of the network input and hidden layer and hidden layer and output are tansig and purelin, respectively, the EMS of the network is below 1.20, and the color difference is between 0.52 and 0. 64, the network has good predictive performance. The average color difference of the test group using the trained network for coloring polyester staple fibers, yarns, and fabrics in the original solution is less than 0.7. indicating that the network training effect is good. The use of neural networks for color prediction presents a new method for color prediction, and the results of this study can provide reference for enterprises to control color changes in textile production processes. [ABSTRACT FROM AUTHOR]