To address the inefficiency of manual color difference classification, this study proposes a multi-strategy improved black-winged kite optimized extreme learning machine (MBKA-ELM) model for dyeing fabric color difference classification. First, as the random initialization of hidden layer weights and biases in extreme learning machine (ELM) algorithms can lead to uneven model training and algorithm instability, the black?‐?winged kite (BKA) optimization algorithm is employed to optimize these key parameters. Second, the incorporation of mirror reverse learning, BKA circumnavigation foraging, and longitudinal and transverse crossover strategies enhances both the convergence speed and global optimization ability of the algorithm. Finally, the MBKA-ELM model is constructed for dyeing fabric color difference classification, achieving an accuracy rate of 98.8% and confirming the feasibility of using this model compared to conventional color difference calculation formulas for detection. Comparative experiments demonstrate the stabilization of the MBKA-ELM model after 10 iterations with a higher classification accuracy than comparable models. Compared with the traditional ELM and optimized models—black-winged kite optimized ELM, spotted emerald optimized ELM, Guanhao pig optimized ELM, cougar optimized ELM, and snake optimized ELM—the classification accuracy improves by 13%, 3.4%, 1.4%, 5%, 4.2%, and 3%, respectively. The proposed model demonstrates superior convergence speed and classification accuracy.