Aiming at the low prediction effect caused by single data source in the current RMB exchange rate forecast research, a forecast technology based on Internet public opinion intensity was proposed. By comparing and analyzing various data sources, the forecast error of RMB exchange rate was effectively reduced. Firstly, the Internet foreign exchange news data and historical market data were fused, and the multi-source text data were converted into the computable vectors. Secondly, five feature combinations based on sentiment feature vectors were constructed and compared, and the feature combination embedded with intensity of Internet public opinion was given as the input of forecast models. Finally, a temporal sliding window of foreign exchange public opinion data was designed, and an exchange rate forecast model based on machine learning was built. Experimental results show that feature combination embedded with Internet public opinion outperforms the feature combination without public opinion by 9.8% and 16.2% in Root Mean Squared Error (RMSE) and Mean Squared Error (MAE). At the same time, the forecast model based on Long Short-Term Memory network (LSTM) is better than that based on Support Vector Regression (SVR), Decision Tree regression (DT) and Deep Neural Network (DNN).