The color-matching model is conducive to expanding the scope of application of colorful fabrics and can speed up the achievement of intelligent production. To solve the problem in which the existing color-matching system of intelligent colored spun yarn cannot be applied to the digital rotor-spinning products of dope dyed viscose fiber, 66 types of mélange yarn were spun with a digital rotor-spinning frame using red, yellow, and blue dope dyed viscose fibers at a ratio gradient of 10%. Furthermore, the knitted fabric samples were produced using a circular machine. Meanwhile, a Datacolor 650 spectrophotometer was used for color testing, and the experimental results were recorded. Based on the color-matching model of the Kubelka–Munk theory, a color-matching model is built based on the experimental results. In addition, the accuracy of the model was analyzed and verified using the least-squares and relative value methods. The results show that, compared with the relative value method, the color-matching model constructed using the absorption coefficient K value and scattering coefficient S value calculated based on the least-squares approach is more accurate. The error between the predicted ratio of the test sample and the actual ratio was only 0.0979, the average color difference was only 0.465, and there were no visible differences between the predicted color of the sample and the actual color.

配色模式有利于扩大彩色面料的应用范围，加快实现智能化生产。为解决现有的智能色纺纱配色系统无法应用于粘胶染色纺粘纤维数码转杯纺产品的问题，采用红色数码转杯纺机纺出66种混色纱，黄色和蓝色原液染色粘胶纤维，比例梯度为 10%。此外，针织物样品是使用圆机生产的。同时使用Datacolor 650分光光度计进行颜色测试，并记录实验结果。基于Kubelka-Munk理论的配色模型，根据实验结果建立配色模型。此外，采用最小二乘法和相对值法对模型的准确性进行了分析和验证。结果表明，与相对值法相比，利用吸收系数构建的配色模型基于最小二乘法计算的K值和散射系数S值更准确。测试样品的预测比例与实际比例的误差仅为0.0979，平均色差仅为0.465，样品的预测颜色与实际颜色之间没有明显差异。