As the demand for customized manufacturing grows, the Distributed Flexible Job Shop Scheduling Problem (DFJSP) involves complex,variable scheduling tasks, dynamic reconfiguration of multi-line manufacturing equipment, and increased collaborative constraints across human, machine, material, method, and environment. Addressing the multi-scenario scheduling requirements is challenging, as single evolutionary algorithms struggle to adapt to diverse scenarios and incur high time costs when assessing such complex constraints. To address this issue, this paper establishes a mathematical model for the DFJSP and subsequently proposes a scheduling algorithm based on the Vector Mapping Surrogate Model (VMSM). 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, VMSM effectively enhances DFJSP solution generation speed by up to 51.26\%, while maintaining the quality of the scheduling solutions.