In recent years, share has become a more and more heated topic in the whole world. As one of the most significant items, the data is featured by great complexity, especially in price prediction. According to the previous relevant study, its prediction has high requirements for the model, which indicates using a single model cannot acquire relatively accurate prediction results. With regard to this problem, integrating random forest and long short-term memory is illustrated to solve that. The first step is the normalization of related share data, which is to reduce the influence caused by the discrepancy of different data. And then, random forest is used for choosing relatively optimal feature rally. In contrast to single decision tree, the application of random forest has simplified the complexity of training. After that, long short-term memory is used for forecasting the price and optimize plentiful important parameters in the model. According to test consequence, the error rate of the integrated model is decreased obviously.