Metals like gold have been traded around the world for ages. Gold, the yellow shiny metal, can maintain the stability of the market, disperse the risk of transfer price, and stabilize the national economy. Therefore, the research and prediction of gold prices is not only of great significance to the countries but also of great significance to individuals. However, compared with other financial time series, research on gold prices is still relatively few and lacks models integrated with deep learning. The purpose of this paper is to establish a new model for the time series prediction of gold prices and at the same time to provide new ideas for future financial time series prediction problems. In this paper, a prediction model based on LSTM (long-term memory Neural Network) is proposed by using the time series of large amounts of financial data as experimental data for prediction. The RMSE (root mean square error), which acts as a loss function, is selected to evaluate the accuracy of our prediction model. The model proposed has a good prediction effect on financial time series.