The invention provides a steam turbine health state prediction method based on E-LSTM. The method comprises steps of collecting turbine operation data from a sensor, and preprocessing the turbine operation data; feeding the preprocessed data into an LSTM network, and performing iterative training for multiple times; inputting a plurality of trained model parameters into a genetic algorithm to serve as an initial population, operating the genetic algorithm, and selecting a model parameter with an optimal effect; performing generalization performance verification on the optimal model by using more steam turbine operation data; and predicting the test data set according to the optimal model parameters, and evaluating model errors. According to the method, the accuracy of model prediction canbe improved, over-fitting can be avoided, and multivariate linear regression prediction can be realized, so that the prediction model has a better fitting effect on real data, the error of manual monitoring can be greatly reduced, the fault diagnosis efficiency can be improved, and the occurrence of faults can be informed in advance. The method can be widely applied to state management of variousfirepower and nuclear power plants and even steam turbines of ships.