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With China"s promotion of the "Marine Power" strategy, the shipping industry has rapidly developed. Maritime traffic volumes have dramatically increased, leading to frequent ship collisions. There is an urgent need for long-term prediction of ship trajectories based on accumulated ship navigation data to enhance awareness of maritime traffic conditions and reduce collision rates. The study first pre-processes Automatic Identification System (AIS) data to remove easily identifiable anomalous points. It proposes a dynamic trajectory decorrelation method based on ship characteristics to remove decorrelated points. Next, a self-adaptive spatiotemporal trajectory clustering method considering behavioral constraints is proposed to mine ship motion patterns. It obtains typical ship behavior patterns. Finally, a motion pattern-based temporal graph neural network model is proposed for long-term prediction of ship trajectories and patterns. The Guangdong-Hong Kong-Macao Greater Bay Area is selected as the test region. Comparative experiments validate the proposed model outperforms traditional models in long-term prediction.