Current collision avoidance algorithms used in autonomous navigation vessels are hindered by limited long-term visibility of dynamic obstacles, resulting in overly cautious and frequent avoidance maneuvers that can actually increase the risk of collisions in complex waterways. Therefore, this paper proposed a novel graph-driven multi-vessel long-term trajectory prediction method (termed GMLTP), which extends the length of real-time and accurate prediction of other vessel trajectories. GMLTP can enhance collision avoidance algorithms by enabling more anticipatory trajectory prediction, ultimately supporting real-time route planning in creating safer, more efficient, and optimized routes. Specifically, GMLTP consists of a multi-graph spatial convolution (MGSC) layer and a probability sparse (ProbSparse) self-attention Transformer. MGSC is a finer spatial interactions extractor, which supports the modeling of spatial dependency evolution on longer sequences and thus improves the perception of long-term trends, while ProbSparse self-attention Transformer is used to reduce the computation of learning long-term global time dependencies and thus extending the predictable length. Extensive experimental results indicate that GMLTP exhibits better accuracy and generalization in prediction tasks beyond 48-timesteps (each time step is 10 s), which can provide better assistance to current real-time route planning tasks.