Atrial　fibrillation　(AF)　is　a　common　arrhythmia　associated　with　major　adverse　cardiovascular　events.　Early　detection　and　short-horizon　risk　prediction　are　therefore　clinically　critical.　Prior　attention-based　electrocardiogram　(ECG)　models　typically　treated　subtype　classification　and　short-horizon　onset　risk　prediction　as　separate　tasks　and　optimized　attention　in　only　one　representational　dimension　rather　than　in　a　coordinated　hierarchy.　We　propose　a　hierarchical　multiattention　temporal　fusion　network　(HMA-TFN).　The　proposed　framework　jointly　integrates　lead-level,　morphology-level,　and　rhythm-level　attention,　enabling　the　model　to　simultaneously　highlight　diagnostically　informative　leads,　capture　waveform　abnormalities,　and　characterize　long-range　temporal　dependencies.　Moreover,　the　model　is　trained　for　dual　tasks-AF　subtype　classification　and　30-min　onset　prediction.　Experiments　were　conducted　on　three　open-source　databases　and　the　Fuzhou　University-Fujian　Provincial　Hospital　(FZU-FPH)　clinical　database,　comprising　thousands　of　dual-lead　ECG　recordings　from　a　diverse　subject　population.　Experimental　results　show　that　HMA-TFN　achieves　95.77%　accuracy　in　classifying　paroxysmal　AF　(PAAF)　and　persistent　AF　(PEAF),　and　96.36%　accuracy　in　predicting　PAAF　occurrence　30　min　in　advance.　Ablations　show　monotonic　gains　as　each　attention　level　is　added,　delivering　14.0%　accuracy　over　the　baseline　for　subtyping　and　5.2%　for　prediction.　Grad-CAM　visualization　highlights　clinically　relevant　features　such　as　absent　P-waves,　confirming　model　interpretability.　On　the　FZU-FPH　clinical　database,　it　achieves　a　generalization　performance　of　94.31%,　demonstrating　its　strong　potential　for　clinical　application.