Abstract: Accurate prediction of shock wave position of hypersonic aircrafts can effectively improve the accuracy and efficiency of computational fluid dynamics (CFD) simulation. On the one hand, orthogonalization and densification of the grid near the shock wave of the hypersonic vehicle can effectively improve the numerical accuracy. On the other hand, using the shock wave position of the hypersonic vehicle to correct the computational grid can speed up the CFD convergence process. A shock wave intelligent prediction method for hypersonic vehicles based on machine learning was proposed, which could efficiently and accurately predict the shock position of the typical hypersonic aircraft shape. Firstly, for the typical hypersonic vehicle shape and typical flight state, numerical methods were used to obtain a convergent flow field. Secondly, the shock wave extraction method based on Mach number contour was used to identify the shock wave surface from the flow field and extract the key points that constitute the shock wave to form training data. After that, the supervised learning method was used to predict the positions of these key points and the quadratic curve was used to fit these key points along the flow direction to form a preliminary shock line family. Finally, based on the typical pressure profile, an image-based neural network was constructed to correct the preliminary shock line family and obtain the three-dimensional shock surface. A large number of experimental results show that the shock wave prediction model can effectively predict the shock wave position of the hypersonic vehicle, and the error between the reconstructed shock wave surface and the extracted shock surface from the CFD results is in the order of 10^-4.