Abstract：We select the Heifangtai Dangchuan #6 landslide body in Gansu province as the study region, which is a typical loess landslide area in Chinese mainland. Three recurrent neural network prediction models of landslide are established using the deep learning framework Tensorflow based on the monitoring data of Beidou and displacement meter, namely, the simple recurrent neural network(SimpleRNN), long short-term memory(LSTM), and gated recurrent unit(GRU). Further, in view of the prominent problems of large subjective impact and low computational efficiency caused by the fact that the parameters of the recurrent neural network are mostly adjusted manually by experience or the grid search method, we introduce a genetic algorithm(GA) to optimize the automatic optimization selection of the parameters of the recurrent neural network. Thereby, three recurrent neural network prediction models of landslide optimized by GA are established, namely, GA-SimpleRNN, GA-LSTM, and GA-GRU. The results show that the improved three recurrent neural network prediction models with automatic optimization of parameters have better prediction performance. The GA-GRU model has the highest prediction accuracy, which is more suitable for the high-precision prediction of the long-time displacement of landslides.