The　real-time　prediction　model　that　can　predict　the　onset　of　paroxysmal　atrial　fibrillation　(PAF)　45　min　in　advance　on　the　one　minute　electrocardiogram　(ECG)　segment　with　8　Hz　sampling　frequency　was　proposed,　for　real-time　and　data-intensive　application　scenarios　such　as　long-term　ECG　monitoring　and　intensive　care　units　(ICU).　The　probabilistic　symbolic　pattern　recognition　method　was　used　to　extract　the　pattern　transition　features　within　one　minute　window　of　down　sampled　ECG　sequence,　reducing　the　calculation　complexity　of　the　model　and　the　demand　for　storage　space,　so　as　to　ensure　the　effect　of　real-time　prediction.　A　hybrid　model　(CNN-LSTM)　of　the　convolutional　neural　network　(CNN)　and　the　long　short-term　memory　(LSTM)　was　proposed　to　extract　local　spatial　features　and　time-dependent　features　implied　in　pattern　transition　features.　An　ensemble　classifier　based　on　CNN-LSTM　was　constructed　to　improve　the　generalization　ability　of　the　model.　Spark　Streaming　technology　was　used　to　read,　write　and　calculate　ECG　streaming　data,　and　low　latency　communication　between　data　and　model　was　realized.　The　accuracy,　sensitivity,　and　specificity　of　the　proposed　model　were　91.26%,　82.21%,　and　95.79%　respectively.　The　average　delay　of　model　processing　was　2　s,　which　can　meet　the　real-time　PAF　prediction　demand.　©　2020,　Zhejiang　University　Press.　All　right　reserved.