Aiming at the predict problems of non-static, highly complex and random fluctuations of stock prices, a combination model based on variational mode decomposition(VMD)-circle sparrow search algorithm(CSSA)-long short-term memory neural network(LSTM) is established. Firstly, 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 the different kinds of errors of the proposed model on multiple stock datasets are minimized, and the proposed model has better fitting in stock price and higher accuracy in complex stock price prediction.