In view of the complicated factors influencing the stock price, we revised the Long Short-Term Memory (LSTM) network, which is commonly used in time series, to predict stock prices under the condition of multivariable.First, the Variance Inflation Factor (VIF) was used to screen variables, and then the adaptive promotion (Adaboost) model was combined to check the importance of characteristic variables.Second, the crawler was used to conduct text analysis of investor sentiment, calculate indicators including sentiment index, and reveal the relationship between them and stock price.Then, prices of three stocks including Gree Electric Appliances, Flyco Electric Appliances and Midea Group were predicted by Multilayer Perceptron (MLP) and LSTM, and the appropriate model was selected as the benchmark model.Finally, indicators of sentiment index and investor concern were added to the benchmark model to construct the LSTM-EM model, and the GM (1, 1) model was used to correct the residual term after considering investor sentiment.The empirical results show that the proposed model can predict the stock price accurately.