To improve the accuracy of runoff prediction, research and propose a runoff prediction method that combines the marine predator algorithm (MPA) and the long and short-term memory (LSTM) neural network. Test MPA and particle swarm optimization (PSO) algorithms through 6 simulation functions, use MPA to optimize key parameters such as the number of neurons in the hidden layer of LSTM, training times, etc., and establish PCA based on principal component analysis (PCA) dimensionality reduction and non-dimensionality reduction processing. -MPA-LSTM and MPA-LSTM runoff prediction models, using the measured data from the Luoque Station in Yunnan Province to train and predict the PCA-MPA-LSTM and MPA-LSTM models, and the results are consistent with PCA-LSTM, LSTM, PCA-MPA-SVM, Compare the training and prediction results of MPA-SVM, PCA-MPA-BP and MPA-BP models. The results show that: 1) MPA simulation effect is better than PSO algorithm, with better optimization accuracy and global search ability. 2) The average relative errors of PCA-MPA-LSTM and MPA-LSTM models for instance fitting and prediction are 1.18%, 2.35%, 1.94%, and 1.96%, respectively. The prediction effect is better than the other 6 models and has better prediction Precision and generalization ability. 3) Using MPA to optimize the key parameters of LSTM can effectively improve the generalization ability and prediction accuracy of LSTM; the prediction accuracy of the data dimensionality reduction model is better than that of the corresponding non-dimensionality reduction model, and the data dimensionality reduction processing can effectively improve the prediction effect of the model.