Abstract: With the development of deep learning,more and more deep learning models are applied to relational extraction tasks.The traditional deep learning model can not solve the long-distance learning task,and the performance of the traditional deep learning model is worse when the noise of text extraction is large.To solve the above two problems,a deep learning model based on bidirectional GRU (gated recurrent unit) neural network and attention mechanism is proposed to extract the relationship between Chinese ship faults.Firstly,by using bidirectional GRU neural network to extract text features,the problem of long dependence of text is solved,and the running time loss and iteration times of the model are also reduced.Secondly,by establishing sentence level attention mechanism,the negative impact of noisy sentences on the whole relationship extraction is reduced.Finally,the model is trained on the training set,and the accuracy,recall,and F1 values are calculated on the real test set to compare the model with existing methods.

Key words: Attention mechanism, Deep learning, Gating cycle unit, Relation extraction, Ship fault