This paper proposes a dynamic robust multi-objective evolutionary optimization algorithm based on multi-scenario modeling, aiming to address dynamic multi-objective optimization problems in practical production. The algorithm treats problems in different environments as different scenarios and establishes multiple scenarios through similarity calculation and scenario clustering. Subsequently, it utilizes an improved multi-scenario multi-objective evolutionary optimization algorithm to find compromise solutions for each scenario. When the environment changes, the algorithm directly applies the compromise solution of the corresponding scenario class as the optimal solution for the new problem, thus speeding up the algorithm's response rate. Through reducing the number of problems in scenario classes and retaining the most representative ones, the algorithm gradually improves its robustness and reduces solution switching costs. Experimental results demonstrate that the proposed algorithm can rapidly respond to environmental changes and enhance solution robustness.