Large　scale　activities　can　cause　a　sudden　increase　and　dissipation　of　traffic　flow　in　the　area　around　the　venue,　resulting　in　occasional　and　uncertain　fluctuations　of　the　road　network　operation　in　the　surrounding　area.　The　existing　methods　are　insufficient　to　capture　the　evolution　mechanism　of　traffic　flow　under　the　influence　of　multidimensional　factors　in　special　events　at　the　prediction　scale.　In　order　to　fully　exploit　the　information　of　time　series　and　influencing　factor　features　of　road　section　speed　and　effectively　deal　with　the　coupling　mechanism　between　different　influencing　features　in　speed　prediction,　this　paper　proposed　a　speed　prediction　model　(MC-LSTM)　combining　Interpretable　Machine　Learning　and　Long　Short-Term　Memory　network.　Firstly,　the　study　combined　the　characteristics　of　large　scale　activities　to　construct　the　set　of　influencing　factors.　Then　it　used　the　XGBoost　algorithm　to　evaluate　the　relative　importance　of　the　impact　of　activity　scale,　nature　and　other　factors　characteristics　on　the　speed　of　road　sections　around　the　venue.　It　quantified　the　synergistic　utility　of　multiple　factors　on　the　operation　state　of　the　road　network　around　the　venue,　fused　LSTM　networks,　considered　the　time-dependent　relationship　of　traffic　state,　captureed　the　temporal　correlation　of　different　historical　periods,　and　accurately　predicted　the　speed　of　road　sections　around　the　venue　during　the　activity.　MC-LSTM　was　validated　by　taking　the　road　network　around　large　scale　activities　venues　in　Beijing　for　six　consecutive　months.　The　results　indicate　that　the　prediction　accuracy　of　the　MC-LSTM　model　can　reach　more　than　94.　5%,　which　is　better　than　that　of　XGBoost　model　considering　multiple　factors　synergism,　LSTM　model　considering　only　single　factor　features　and　the　LSTM　model　not　considering　external　features.　It　proved　that　the　model　proposed　in　this　paper　has　better　validity　and　stability.　This　study　can　provide　a　decision　basis　for　optimizing　the　traffic　organization　of　the　road　network　around　the　large　scale　activities　venues　and　formulating　traffic　control　and　security　measures.　©　2024　South　China　University　of　Technology.　All　rights　reserved.