The Industrial Internet integrates industrial systems with advanced Internet technologies to establish an intelligent implementation platform and diversified service ecosystem for cloud manufacturing. However, the extensive user base introduces substantial uncertainties in temporal, financial, and operational requirements of cloud manufacturing tasks. While existing studies propose various solutions, their reliance on subjective criteria for demand variation analysis leads to inadequate handling of fuzzy demands. A novel fuzzy demand-oriented optimization method is proposed for cloud manufacturing service composition, employing fuzzy sets and membership functions to establish an objective quantification framework for modeling uncertain demands. The approach formulates a multi-objective optimization model incorporating four key metrics: service cost, service time, service quality, and resource utilization rate, with fuzzy satisfaction functions constructing constraints containing random variables to ensure robust realization of fuzzy demands. An enhanced NSGA-III algorithm is developed featuring opposition-based learning mechanisms and GKM-based reference point generation to enhance population diversity and convergence efficiency. Validation through benchmark functions and practical cloud manufacturing scenarios confirms the method's effectiveness in addressing fuzzy demand challenges, with the co-evolution algorithm demonstrating superior convergence and diversity performance.