Aircraft represent one of the most significant technological advancements in human history. Aircraft not only provide convenient and rapid transportation, but also serve as critical tools for exploring the natural world. During the early stages of aircraft design, aerodynamic coefficients must undergo meticulous calculations and analysis to ensure the development of safe, reliable, and efficient aircraft. Generally, aerodynamic coefficients are obtained through numerical simulations using computational fluid dynamics and wind tunnel testing. These methods are effective and accurate; however, they often consume significant amounts of time and resources. With advancements in AI, deep learning techniques have been increasingly applied in aerodynamics research. To reduce the time and cost spent on wind tunnel testing during the aircraft design phase, we apply deep learning techniques to high-performance numerical wind tunnels to analyze aerodynamics in the aviation field. A backpropagation neural network model with an error compensation mechanism is created to enhance the efficiency of analyzing and validating aerodynamic coefficients. This approach minimizes the reliance on physical wind tunnel testing, thereby reducing overall development costs.