[Objective] Black-box optimization algorithms are widely used in various complex engineering design scenarios. However, their inherent computational complexity and internal operational opacity result in intensive computing consumption. In the optimal design of motors, black-box optimization algorithms require numerous iterative computations and finite element simulations to screen optimal structural parameters, which significantly extends the design cycle and restricts engineering application efficiency. Although traditional surrogate models can reduce computational costs, they exhibit poor adaptability and struggle to construct universal databases for multiple operating conditions. Slight changes in design requirements or application scenarios necessitate data recollection and model retraining, leading to inherent engineering drawbacks including weak generalization capability and low data reusability. [Methods] To solve the above problems, a black-box optimization algorithm based on transfer surrogate model (TSM) was proposed. Firstly, a novel TSM was constructed based on transfer learning theory. By autonomously screening high-quality and effective samples from the source domain, the inherent data features were deeply mined to improve the prediction accuracy and generalization ability of the model under the small-sample conditions of the target domain. Subsequently, the optimized TSM was deeply integrated with the iterative optimization algorithm to eliminate redundant computational steps during the optimization process and further enhance the execution efficiency of the optimization task. Finally, simulation tests were conducted to verify the practical effectiveness of the proposed algorithm. [Results] The simulation results showed that, compared with the traditional optimization algorithm, the proposed optimization algorithm based on TSM effectively reduced computational consumption, significantly improved the overall efficiency of motor parameter optimization, and accelerated the iterative convergence speed of the model. The proposed algorithm exhibited superior stability under variable motor design conditions. [Conclusion] The proposed optimization algorithm based on TSM addresses the inherent shortcomings of traditional black-box optimization and conventional surrogate models from the perspectives of data utilization and algorithm integration. It breaks through the limitations of poor scenario adaptability and high retraining costs existing in traditional models. This scheme can efficiently adapt to the variable design requirements of practical engineering scenarios. It provides reliable and efficient technical support for the intelligent optimal design of motors and other complex engineering black-box optimization problems, and possesses excellent engineering applicability and popularization prospects.