In the process of landslide surface displacement monitoring, the original data may randomly have long time series missing due to the interference of abnormal equipment work or bad weather, and such data has a great impact on on the landslide warning and prediction. To solve these problems, this paper proposes a data interpolation method based on principal component analysis (PCA) and long short-term memory (LSTM). Firstly, PCA is used to realize the downscaling and feature extraction of landslide monitoring data to eliminate the correlation between data, and then an LSTM-based interpolation model of surface displacement monitoring data is established to interpolate the missing data; finally, the interpolation of surface displacement data is realized. The experimental results show that this model outperforms other machine learning interpolation models such as Back Propagation neural network with mean absolute error, root mean square error and mean absolute percentage error of 0.523, 1.233 and 0.009, respectively. This model can better solve the problem of missing long time series data of surface displacement.