Sea surface temperature (SST) is an important indicator for balancing the surface energy and measuring the sea heat, the high-precision prediction of SST is of great significance to global climate, marine environment, and fisheries. Under extreme climatic conditions, the SST sequence presents obvious non-stationarity, traditional methods are difficult to predict sea surface temperature (SSTP) and have low accuracy. The non-stationarity of the SST subsequence decomposed based on the empirical mode decomposition (EMD) algorithm is significantly reduced, and the gated recurrent unit (GRU) neural network, as a common machine learning prediction model, has fewer parameters and faster convergence speed, so it is not easy to over fit in the training process. Combining the advantages of the EMD model and the GRU model, a SST prediction model based on EMD-GRU is proposed. In order to verify the prediction effect of the proposed model, several groups of comparative experiments were carried out on five SST sequences with different lengths. Experimental results show that the multi-scale complexity of the prediction results of the proposed model is lower in comparison with directing application of recurrent neural network (RNN), long-short term memory (LSTM), and GRU models, and the mean square error (MSE) and mean absolute error (MAE) of the prediction results of the proposed model have been reduced. In order to verify the influence of data sequence length on prediction accuracy, a supplementary experiment is designed. The longer the prediction length, the worse the accuracy effect; after the sequence is processed by EMD algorithm, the effect is improved, and the effect is improved obviously when the prediction length becomes longer.