In view of the high design dimension and difficulty in constructing surrogate models in the aerodynamic optimization of three-dimensional turbine blades， a multi-degree-of-freedom parameterized dimensionality reduction method was proposed to construct an efficient optimization framework， that integrated DFFD and PCA， and combined the pre-screened surrogate model assisted differential evolution （Pre-SADE） algorithm. Taking a small gas turbine as the object， a snapshot set was generated through experimental design， and the 36-dimensional DFFD design space was mapped to the 10-dimensional basis modal coefficient space. A concise and effective surrogate model was established in the dimensionality reduction space and rapid optimization was completed. The results show that the proposed method significantly reduces the shock wave intensity and aerodynamic loss while improving the design point flow （+0.46%） and isentropic efficiency （+3.191%）， and the optimization time is reduced by 24.58%. The study verifies the intuitiveness， effectiveness and optimization efficiency improvement advantages of this dimensionality reduction method in high-dimensional design problems， providing a more efficient and low-cost solution for blade aerodynamic optimization.
