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Aiming at the problem of single-point GPS positioning error when using GPS receivers for landslide displacement monitoring, a landslide displacement monitoring algorithm based on GPS and neural network is proposed. Before the landslide and landslide occurred, the positioning data measured by GPS receivers are coupled together and are not linearly separable. The neural network with non-linear separable features is used to divide the coupled positioning data into two classifications, one belongs to the non-landslide GPS data and the other belongs to the landslide GPS data, which avoids the accurate modeling of GPS nonlinear and non-Gaussian positioning error. The sample training set measured by the GPS receiver is used to train the neural network, and the trained neural network model is used to verify the classification effect of the test set. The experimental results show that for the low-precision GPS receivers, when the landslide reaches 3 meters, 5 meters and 8 meters respectively, the correct rates of training samples classification are 95.85%, 99.23% and 99.99% respectively, and the correct rates of testing samples classification are 82.94%, 89.44% and 91.05% respectively, indicating that the proposed algorithm is suitable for a greater degree of landslide.