Indoor　airflow　distribution　significantly　influences　temperature　regulation,　humidity　control,　and　pollutant　dispersion,　directly　impacting　thermal　comfort　and　occupant　health.　Accurate　and　efficient　prediction　of　airflow　fields　is　essential　for　optimizing　ventilation　systems　and　enabling　real-time　control.　However,　existing　computational　approaches　for　dynamic　ventilation　are　computationally　intensive　and　have　limited　generalization　capabilities.　This　study　leverages　the　Fourier　neural　operator　(FNO),　a　method　rooted　in　operator　learning　and　Fourier　transform　principles,　to　develop　a　three-dimensional　(3D)　airflow　simulation　model　capable　of　predicting　velocity　and　its　components.　The　model　was　trained　using　200　s　of　sinusoidal　ventilation　data　(amplitude:　0.4)　and　evaluated　under　diverse　air　supply　patterns,　including　sinusoidal　(amplitude:　0.8),　intermittent,　and　stepwise　periodic　ventilation.　Additionally,　the　model＇s　performance　was　assessed　with　low-resolution　training　data　and　further　tested　for　recursive　prediction　accuracy.　Results　reveal　that　the　FNO　method　achieves　high　accuracy,　with　a　mean　square　error　of　9.906　x　10-5　for　sinusoidal　amplitude　0.8　and　4.004　x　10-5　over　400　time　steps　for　sinusoidal,　intermittent,　and　stepwise　conditions.　Further　evaluations,　including　tests　on　low-resolution　training　data　and　recursive　prediction,　were　conducted　to　examine　the　model＇s　resolution　invariance　and　assess　its　performance　in　iterative　forecasting.　These　findings　demonstrate　the　FNO　method＇s　potential　for　robust,　efficient　prediction　of　3D　unsteady　airflow　fields,　providing　a　pathway　for　real-time　ventilation　system　optimization.