The geological conditions and operating environment of the protective embankment in the Longxikou reservoir area are complex,and how to use the original monitoring data to achieve online monitoring of its safe operation status is a key issue that needs to be urgently solved.In response to the problems of poor generalization and low accuracy in traditional monitoring models for monitoring sequences with strong nonlinear features,a long short-term memory (LSTM) online deformation monitoring model for the protective embankment based on LSTM algorithm is built,with water level,temperature,and time as inputs and deformation as outputs.By comparing and analyzing the influence of different model parameters on the accuracy,the multi-parameter sensitivity law of learning rate>block size>maximum number of iterations>number of hidden layer units is revealed,and the recommended values of relevant parameters are proposed.Engineering applications show that the model has high accuracy,strong applicability,and stability.