This　research　examines　the　predictive　effect　of　real-time　investor　sentiment　on　high　-frequency　stock　returns.　Utilizing　text　sentiment　analysis,　we　extract　investor　sentiment　with　a　half-hour　frequency　from　the　stock　message　board.　The　RR　-MIDAS　method　is　used　to　model　half-hourly　sentiment　and　three　-minute　stock　returns,　and　economic　analysis　reveals　that　investor　sentiment　significantly　affects　the　stock　returns　during　seven　high　-frequency　periods,　and　the　influence　gradually　weakens.　Subsequently,　we　propose　the　＂MF-EEMD-ML＂　prediction　system,　which　introduces　a　rolling　decomposition　algorithm　into　the　RR　-MIDAS　framework　for　predicting　highfrequency　trend　items　combined　with　real-time　forum　sentiment.　The　results,　using　rolling　EMD　decomposition　for　comparison,　show　that　the　＂MF-EEMD-ML＂　system　achieves　a　maximum　reduction　of　19.18　%　in　MAE,　19.08　%　in　RMSE,　11.71　%　in　SMAPE,　and　a　maximum　improvement　of　16.66　%　in　DS.　Additionally,　the　outcomes　of　the　Diebold　-Mariano　(DM)　tests　also　demonstrate　that　the　＂MF-EEMD-ML＂　prediction　system　significantly　outperforms　both　the　＂MF-EMD-ML＂　system　and　the　LR　model.