Abstract: The disclosure discloses a ship motion prediction method based on long short-term memory network and Gaussian process regression. The method includes: normalizing acquired ship motion historical data under a certain degree of freedom to form a ship motion original time series; dividing the original time series into a training set and a test set; reconstructing a data set according to the training set and the test set, and establishing a long short-term memory (LSTM) network model for prediction to obtain prediction results of the first ship motion; reconstructing a data set, and establishing a Gaussian process regression (GPR) model for prediction to obtain prediction results of the second ship motion; and denormalizing the prediction results obtained by the Gaussian process regression model to obtain final ship motion prediction results.