Abstract: Dissolved oxygen (DO) content is a critical factor that affects the healthy growth of aquatic products in aquaculture ships. Accurate prediction of DO content is necessary to improve aquatic production and quality. To increase the accuracy of DO prediction , based on the data collected from a Trachinotus ovatus culture experiment, we established a hybrid model for DO prediction in aquaculture ships by applying the convolutional neural network (CNN) and gated recurrent unit (GRU) methods. Based on Pearson correlation analysis, we selected four predictors, namely dissolved oxygen content, temperature, pH value and circulating water flow, which were trained and calibrated to predict the DO content. The model proposed in this paper outperformed CNN, GRU and long short-term memory (LSTM) models in all evaluation indexes, and its root mean square error (RMSE), mean absolute error (MAE) and determination coefficient R² were 0.119, 0.084 and 0.976, respectively. The results indicate that the model proposed in this paper has the greatest prediction precision and can meet the demand for DO content prediction in actual production of aquaculture ships, which provides references for monitoring and early warning of DO content in the production process of aquaculture ships.