The factors that affect stock price are very complicated. Therefore, this paper revises the LSTM, which is commonly used in time series, and selects stock price for prediction under the condition of multivariable. First, variance inflation factor（VIF） was used to screen variables, and then the adaptive promotion method (Adaboost) model is combined to check the importance of characteristic variables. Secondly, the crawler is used to conduct text analysis of investor sentiment, calculate sentiment index and other indicators, and reveal the relationship between them and stock prices. Then, three different stock prices of Gree Electric Appliances, Flyco Electric Appliances and Midea Group were predicted, multi-layer perceptron（MPL）model and LSTM model were compared, and the appropriate model was selected as the benchmark model. On the basis of the benchmark model, the LSTM-EM model was constructed by adding sentiment index, investor attention and other indicators. Furthermore, GM (1,1) model was used to correct the residual term after considering investor sentiment. The empirical results show that the model can predict the stock price accurately.