<p>Traditional multi-criteria decision-making (MCDM) methods in blended textiles always may face difficulties in reconciling expert judgments with data-driven uncertainty arising from nonlinear fiber interactions. To address these issues, a hybrid blended yarn assessment framework, integrating Fuzzy Analytic Hierarchy Process with nonlinear dynamic Fuzzy TOPSIS (FAHP-ND-FTOPSIS), is introduced to mitigate fuzzy uncertainty and dynamic variations. In our method, nonlinear penalties with dynamic parameterization are first merged to amplify informative deviations while diminishing cluster adhesion, achieving elevated coefficient of variation and enhanced tier robustness. Subsequently, dynamic adjustment of distribution parameters is further utilized to reduce score clustering and improve sample differentiation. Experimental results are demonstrated that our framework could enhance the discriminative capability, achieving a 79.5% increase in the coefficient of variation compared with the existing method. Notably, case analysis is also indicated that scores of underperforming yarns can reduce by 83.4%, while maintaining 66.6% of the gaps between quality tiers.</p>