Trajectory prediction is a critical technology for ensuring ship navigation safety, improving the efficiency of marine traffic control, and efficiently searching for maritime targets. This research proposes an Improved Social-STGCNN (IS-STGCNN) to increase the accuracy of ship trajectory prediction. This technique successfully combines the benefits of the spatio-temporal graph convolutional neural network (STGCNN) for extracting ship trajectory features and the benefits of the time-extrapolated convolutional neural network (TXP-CNN) for generating future ship trajectory using the obtained features. This method proposes a social-sampling strategy based on prior knowledge, i.e., the bumper model of ship safety assessment, to carry out negative sampling and enhances the effectiveness of the IS-STGCNN method in learning the concept of negative examples (such as collisions). The interaction force between ships is estimated in a novel way in the spatio-temporal graph convolutional neural network to improve high-precision feature extraction from ship trajectory. Model predictive control (MPC) is used to correct the output of trajectory prediction in order to fulfill kinematic constraints and assure prediction kinematic feasibility. The experiments are carried out using AIS data, and the findings reveal that IS-STGCNN outperforms the state-of-the-arts.