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In order to further improve the prediction accuracy of dissolved oxygen in aquaculture, this paper proposes a dissolved oxygen prediction model based on cross-validation grid optimization GBDTLSTM. First, the Gradient Boosting Decision Tree ( GBDT) is used to select characteristic factors that have a high degree of influence on dissolved oxygen. Then the Long Short-Term Memory Network (LSTM) is built based on the Keras deep learning framework, and the cross-validation grid optimization algorithm is used to optimize the LSTM parameters. Finally, the model in this paper is applied to a standard pond in the fishery base of Jintan City, Jiangsu Province to pr edict dissolved oxygen. The experimental results show that the mean square root error (RMSE), mean absolute error (M AE ) and mean relative error mean (MAPE) of the model are 0.208,0.158 and 2.635, respectively. The evaluation indexes are better than other comparison prediction models. It shows that the model has good predictive ability and generalization ability, which can meet the actual needs of modern aquaculture. [ABSTRACT FROM AUTHOR]