Traditional aerodynamic optimization design requires multiple time-consuming CFD analyses; while widely-used surrogate-based optimization (SBO) can effectively reduce the number of CFD analyses, but it cannot speed up a single CFD analysis. Hence, this paper proposes a hot-start strategy using the proper orthogonal decomposition-back propagation based neural network (POD-BPNN), and applies it in surrogate-based aerodynamic optimization. First, the POD-BPNN model is built from geometric design variables to flowfield data through the initial samples in an SBO. Then, during iteration of the SBO, the flowfield of a new sample is predicted by the built model, and is used as the initial flowfield of the hot-start CFD analysis for the new sample. At the same time, the new sample is used to update the POD-BPNN model until the end of the optimization. The results of the aerodynamic optimization design of the transonic airfoil indicate: the hot-start strategy using the POD-BPNN can reduce the time of a single CFD analysis by 68%, and improves the efficiency of the SBO by 37%.