Starting from a C/C++ behavioral-level programming language, High-Level Synthesis (HLS) is a solution for rapid prototyping of application-specific hardware, where designers can apply HLS directives to optimize hardware implementations by trading-off between cost and performance. However, current HLS tools do not provide reliable Quality of Results (QoR) estimations, which prevents quick and precise design-space exploration. Under the background of the widespread use of machine learning (ML) to improve the predictability of EDA tools, this work proposes a Graph Neural Network (GNN) based predictive model that can accurately predict post-route QoR. The experimental results demonstrate that the predictive model not only outperforms state-of-the-art HLS tool in terms of QoR estimation accuracy but also speed.