To enhance the design efficiency of inward-turning inlet and enable rapid prediction of internal contraction basic flowfield, a parametric design of internal contraction basic flowfield was implemented using quasi-uniform B-spline methods, and a flow field prediction model based on deep learning residual neural networkarchitecture was proposed. The predicted flowfields were quantitatively evaluated using image quality assessment methods including PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index), from which key flow field characteristics such as wall property distributions and shock wave shape were extracted to achieve the goal of rapidly obtaining flow field contours and characteristic parameter distributions based on basic flowfield geometric parameters. Research result shows that the constructed flow field rapid prediction model is characterized by high accuracy, with an overall average PSNR of 42.51 dB and an average SSIM of 0.997 3. Key characteristics and parameter distributions are effectively extracted from the prediction results, providing strong support for the rapid design and optimization of the internal contraction basic flowfield.