To address the issues of difficult prediction of landslide displacement and difficulty in selecting influencing factors， a model combining Double Moving Average （DMA）， Variational Modal Decomposition （VMD）， Improved Gray Wolf Optimizer （IGWO） algorithm and Support Vector Regression （SVR） was proposed for landslide displacement prediction. Firstly， DMA was used to extract the trend and periodic terms of landslide displacement， and polynomial fitting was used to predict the trend term. Secondly， the influencing factors of the landslide periodic term were classified， and VMD was used to decompose the original factor sequence to obtain the optimal sequence. Then， a grey wolf optimizer algorithm combining SVR with an improved Circle-based multi-tactic， called CTGWO-SVR （Circle Tactics Grey Wolf Optimizer with SVR）， was proposed to predict the landslide periodic term. Finally， the cumulative displacement prediction sequence was obtained using a time series additive model， and the model was evaluated using post validation difference verification and small probability error in grey prediction. Experimental results show that compared with GA （Genetic Algorithm）-SVR and GWO-SVR models， CTGWO-SVR has higher prediction accuracy with a fitting degree of 0.979， and the Root Mean Square Error （RMSE） reduces by 51.47% and 59.25%， respectively. The model evaluation accuracy is level one， which can meet the real-time and accuracy requirements of landslide prediction.