The　Fintech　index　has　been　more　active　in　the　stock　market　with　the　Fintech　industry　expanding.　The　prediction　of　the　Fintech　index　is　significant　as　it　is　capable　of　instructing　investors　to　avoid　risks　and　provide　guidance　for　financial　regulators.　Traditional　prediction　methods　adopt　the　deep　neural　network　(DNN)　or　the　combination　of　genetic　algorithm　(GA)　and　DNN　mostly.　However,　heavy　computational　load　is　required　by　these　algorithms.　In　this　paper,　we　propose　an　integrated　artificial　intelligence-based　algorithm,　consisting　of　the　random　frog　algorithm　(RF),　GA,　and　DNN,　to　predict　the　Fintech　index.　The　proposed　RF-GA-DNN　prediction　algorithm　filters　the　key　input　variables　and　optimizes　the　hyperparameters　of　DNN.　We　compare　the　proposed　RF-GA-DNN　with　the　traditional　GA-DNN　in　terms　of　convergence　time　and　prediction　accuracy.　Results　show　that　the　convergence　time　of　GA-DNN　is　up　to　20　hours　and　its　prediction　accuracy　is　97.4%.　In　comparison,　the　convergence　time　of　our　RF-GA-DNN　is　only　about　1.5　hours　and　the　prediction　accuracy　reaches　97.0%.　These　results　demonstrate　that　the　proposed　RF-GA-DNN　prediction　algorithm　significantly　reduces　the　convergence　time　with　the　promise　of　competitive　prediction　accuracy.　Thus,　the　proposed　algorithm　deserves　to　be　widely　recommended　for　predicting　the　Fintech　index.