Abstract: The application of machine learning in airfoil aerodynamic calculation and inverse design methods was studied in this paper, with the training and optimization of artificial neural networks in the larger airfoil space realized. The airfoil aerodynamic calculation model was constructed, and an airfoil inverse design optimization model with targeted pressure distribution was established, which gives rise to the potential usage in the engineering field. As a research hotspot in the field of machine learning, the research work of artificial neural network has gained increasingly interests, and some researchers have tried to apply it to the subject of fluid mechanics. In this paper, the application of artificial neural network in the field of airfoil calculation was studied as follows. Firstly, the airfoil library with a certain airfoil space range was constructed around a reference airfoil by Parsec parameterization method. Secondly, the numerical simulation was performed using XFOIL, generating a CFD library, which is mapped to the airfoil library one-by-one. Lastly, through training and optimization of neural networks, a fast and highly reliable airfoil aerodynamic prediction method was realized, and a new airfoil optimization design method was introduced. Through the automated programming, the generations of different sample-size databases were easily constructed, and the training and optimization process of the neural network under different sample-size of the airfoil space was investigated. Experimental results show that in the field of machine learning, the accuracy of the airfoil inverse design model based on neural network is highly dependent on the size and coverage of samples.