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A ship trajectory prediction model based on attention mechanism time-domain convolutional network and bidirectional long short memory network (TCN-ABiLSTM) is proposed to address the issue of low prediction accuracy in existing ship trajectory prediction models. Firstly, TCN network is constructed to extract the sequence features of ship trajectories. Then, attention mechanism is introduced into the network to adjust the weights of different attribute features, highlighting the features that have a greater impact on trajectory prediction. Finally, Bi-LSTM network is constructed to learn the pre and post situation of trajectory sequences to extract more information from the sequences, achieving prediction of future ship trajectories; Training and testing experiments are conducted on the network using actual ship AIS data. The experimental results show that the TCN-ABiLSTM model has higher accuracy and better fit in predicting ship trajectories compared to LSTM, Bi LSTM, TCN, BiLSTM Attention, and TCN-Attention models. This verifies the effectiveness and practicality of the designed TCN-ABiLSTM model in predicting ship trajectories.