The　short-term　large　scale　activities　refer　to　various　large-scale　activities　with　a　duration　of　several　hours,　with　features　of　high　peak　passenger　flow　and　short　gathering　time.　The　analysis　of　public　transport　passenger　flow　characteristics　and　travel　demand　prediction　for　large-scale　activities　can　provide　a　targeted　organization　plan　for　public　transportation　security　in　the　context　of　large-scale　activities.　Based　on　the　smart　card　data　of　Beijing,　the　paper　analyzes　the　spatial-temporal　characteristics　of　passenger　flow　under　the　background　of　large-scale　activities.　The　Discrete-Fourier　transform　is　used　to　study　the　frequency　domain　characteristics　of　large-scale　active　passenger　flow　sequences.　Then,　through　the　steps　of　sampling,　decomposition　and　reconstruction　of　passenger　flow　sequence　features,　the　public　traffic　passenger　flow　prediction　model　for　short-term　large　scale　activities　based　on　Wavelet　analysis　was　established.　And　reconstruction　steps　to　establish　a　short-term　large-scale　public　transport　passenger　flow　forecasting　method　based　on　wavelet　analysis.　The　method　overcomes　the　weaknesses　that　data　detail　information　are　ignored　in　large-scale　forecasting　during　modeling,　and　improves　the　stability　of　forecasting　results　in　short-term　forecasting.　A　case　study　of　Beijing　was　conducted　to　validate,　and　the　result　shows　that　the　mean　absolute　percentage　error　(MAPE)　and　mean　absolute　error　(MAE)　are　0.22%　and　1.47%,　respectively.　©　2020,　Springer　Nature　Singapore　Pte　Ltd.