To address the problems of stock price prediction due to its non-static,highly complex and random fluctuations,a combination model based on Variational Mode Decomposition (VMD)-Circle Sparrow Search Algorithm (CSSA)-Long Short-Term Memory (LSTM) neural network is established.The original stock closing data is decomposed into several Intrinsic Mode Function (IMF) components by VMD,and then the CSSA is used to optimize the parameters of hidden layer neurons,iteration number and learning rate of LSTM,and the optimal parameters are fitted into the LSTM,where each IMF component is modeled and predicted,and the prediction results of IMF component are superimposed to obtain the final result.Experiments show that the RMSE,MAE and MAPE of the proposed model are minimized on multiple stock datasets,the error of the predictied closing prices of individual stocks fluctuates around 0,which is more stable with better fitting and higher accuracy.