Abstract: Due to the shortcomings of numerical modeling and statistical learning methods in SST modeling, this study applies LSTM-RNN (long short term memory recurrent neural network) to improve the SST modeling. Using SST, solar radiation, wind field, evaporation，precipitation and other physical parameters of monthly averaged data of 24 years, the SST time series model of the Western Pacific is constructed by LSTM-RNN to predict the coming month's SST in the study area. Two models, model1 and model2, are established. Model1 only uses SST data as a comparison of model2 that consists of SST and physical parameters. The results show that the MAE of model2 in the valid set is 0.15℃, RMSE is 0.19℃ and the correlation coefficient is 0.978. Compared with model1, the overall accuracy of model2 is higher than 31%. It shows that the application of LSTM-RNN to SST modeling is feasible and LSTM-RNN can get the relationship between physical parameters and SST. Thus, the accuracy of the surface temperature model of sea water can be improved significantly.