In order to further improve the accuracy and efficiency of predicted compressible flowfields arounds airfoils at high Reynolds number， large angle of attack （AoA）， and high Mach numbers， a machine learning inference method based on coordinate transformation method and UNet neural network is propesed. Firstly， a novel coordinate transformation method for data pre-processing is developed. This method transforms physical quantities and grid information in computational fluid dynamics into spatial information of neural network， making the distribution of flowfield information more in line with the input requirements of the neural network. Secondly， an improved deep UNet neural network is established， allowing the model to learn the fine and complex localized flow characteristics of the airfoil flowfield. The two innovative methods are combined to establish a machine learning inference method for the compressible flowfield of airfoils， and a fast and high-precision inference model is obtained. Finally， the flowfields and aerodynamic forces of different types of airfoils are predicted and analyzed， and results are compared with those from traditional machine learning method. The results show that the machine learning inference method proposed in this paper can better predict the compressible flowfield of airfoils， and can better capture the complex flow behavior at high Reynolds numbers， and predict the flow separation and shock waves phenomena under high Mach numbers with large AoA.