The　growing　complexity　of　the　transport　system　has　led　to　a　surge　in　traffic　pressure,　accidents　and　a　deteriorating　traffic　environment.　Real-time　and　accurate　traffic　flow　forecasting　capabilities　are　required　to　achieve　timely　traffic　guidance,　improve　road　safety　and　travel　efficiency,　and　enhance　the　traffic　environment.　One　of　the　reasons　for　the　difficulty　in　predicting　traffic　flows　is　that　traffic　conditions　are　highly　susceptible　to　a　variety　of　factors　such　as　weather　and　traffic　accidents,　so　there　is　a　need　for　a　method　of　predicting　traffic　flows　that　incorporates　a　variety　of　influences.　This　paper　proposes　that　weather-related　data,　traffic　incident　effects　and　time-series　data　form　a　multidimensional　factor　to　accurately　predict　short-time　traffic　flows.　This　paper　combines　the　advantages　of　Long　Short-Term　Memory　Network(LSTM)　in　multidimensional　data　processing　with　the　powerful　past　and　future　information　extraction　capability　of　Bi-directional　Gated　Recurrent　Unit(Bi-GRU),　while　introducing　an　attention　mechanism　to　better　capture　the　impact　of　different　moments　on　the　prediction　results,　and　proposes　a　Multidimensional　Factor　Fusion　Network(MFFN).　The　model　is　able　to　take　full　account　of　multiple　factor　influences　and　multiplex　data　to　provide　highly　accurate　forecasting　results.　©　2023　IEEE.