As the demand for customized manufacturing grows, the distributed flexible job shop scheduling problem (DFJSP) involves complex and variable scheduling tasks, dynamic reconfiguration of multi-line manufacturing equipment, and increased collaborative constraints across human, machine, material, method, and environment. Traditional evolutionary algorithms struggle to adapt to multi-scenario scheduling and are inefficient due to high computational costs. To address this, this paper establishes a mathematical model for the DFJSP and subsequently proposes a scheduling algorithm based on the vector mapping surrogate model(VMSM). The VMSM enhances the recognition of historically similar problems through high-dimensional feature vector mapping, and aids in generating initial solutions and evaluating solution sets within evolutionary algorithms, thereby improving search efficiency. Experimental results demonstrate that this method increases classification accuracy for similar scheduling solutions by 25%-35%, significantly reducing the number of initial solution selections and solution set evaluations. Under various scenario complexities, the VMSM effectively enhances DFJSP solution generation speed by up to 51.26 %, while maintaining the quality of the scheduling solutions.