Objective Accurate prediction of hemodynamic parameters in stenotic blood vessels holds critical clinical significance for the diagnosis and treatment of cardiovascular diseases such as atherosclerosis. To address the limitations of conventional Physics-Informed Neural Networks in handling hemodynamic boundary constraints, this study proposes an improved hard boundary-constrained physics-informed neural network (HBC-PINN) framework to achieve precise prediction of blood flow fields within stenotic arteries, providing new perspectives for the development of efficient and reliable biomedical fluid numerical calculation methods. Methods An idealized stenosed vessel geometry model was established and CFD simulation was performed to obtain a validation dataset. Appropriate boundary dependent trial functions were designed according to the hard constraint method to embed the flow boundary conditions into the network output. Thus, an HBC-PINN model with the hard boundary constraint method was constructed to predict the velocity field and pressure field of stenosed blood flow. Meanwhile, an original PINN model with the soft constraint method was also built for comparison. By evaluating the accuracy of the two models on the validation dataset, we verified the capability of the HBC-PINN model to simulate hemodynamics without using any labeled data for training. Results The effectiveness of the HBC-PINN method in predicting hemodynamic parameters in stenosed blood flow tasks has been validated. The relative L2 errors of the flow velocity and pressure predicted by the HBC-PINN in two different stenosis scenarios were both lower than 0.5%, representing an improvement of over 48.8% in accuracy compared to the original PINN model. Additionally, the prediction accuracy of the transverse velocity also increased by more than 35.4%. Conclusions Implementing hard constraints on boundary conditions in the PINN modeling process can effectively improve the prediction accuracy of hemodynamic parameters and the efficiency of model solving.