Atrial　fibrillation　(AF)　is　the　most　common　cardiovascular　disease　(CVD),　and　most　existing　algorithms　are　usually　designed　for　the　diagnosis　(i.e.,　feature　classification)　or　prediction　of　AF.　Artificial　intelligence　(AI)　algorithms　integrate　the　diagnosis　of　AF　electrocardiogram　(ECG)　and　predict　the　possibility　that　AF　will　occur　in　the　future.　In　this　paper,　we　utilized　the　MIT-BIH　AF　Database　(AFDB),　which　is　composed　of　data　from　normal　people　and　patients　with　AF　and　onset　characteristics,　and　the　AFPDB　database　(i.e.,　PAF　Prediction　Challenge　Database),　which　consists　of　data　from　patients　with　Paroxysmal　AF　(PAF;　the　records　contain　the　ECG　preceding　an　episode　of　PAF),　and　subjects　who　do　not　have　documented　AF.　We　extracted　the　respective　characteristics　of　the　databases　and　used　them　in　modeling　diagnosis　and　prediction.　In　the　aspect　of　model　construction,　we　regarded　diagnosis　and　prediction　as　two　classification　problems,　adopted　the　traditional　support　vector　machine　(SVM)　algorithm,　and　combined　them.　The　improved　quantum　particle　swarm　optimization　support　vector　machine　(IQPSO-SVM)　algorithm　was　used　to　speed　the　training　time.　During　the　verification　process,　the　clinical　FZU-FPH　database　created　by　Fuzhou　University　and　Fujian　Provincial　Hospital　was　used　for　hybrid　model　testing.　The　data　were　obtained　from　the　Holter　monitor　of　the　hospital　and　encrypted.　We　proposed　an　algorithm　for　transforming　the　PDF　ECG　waveform　images　of　hospital　examination　reports　into　digital　data.　For　the　diagnosis　model　and　prediction　model　trained　using　the　training　set　of　the　AFDB　and　AFPDB　databases,　the　sensitivity,　specificity,　and　accuracy　measures　were　99.2%　and　99.2%,　99.2%　and　93.3%,　and　91.7%　and　92.5%　for　the　test　set　of　the　AFDB　and　AFPDB　databases,　respectively.　Moreover,　the　sensitivity,　specificity,　and　accuracy　were　94.2%,　79.7%,　and　87.0%,　respectively,　when　tested　using　the　FZU-FPH　database　with　138　samples　of　the　ECG　composed　of　two　labels.　The　composite　classification　and　prediction　model　using　a　new　water-fall　ensemble　method　had　a　total　accuracy　of　approximately　91%　for　the　test　set　of　the　FZU-FPH　database　with　80　samples　with　120　segments　of　ECG　with　three　labels.