A depth learning-based road network traffic situation forecast method comprises the following steps of S1, acquiring multi-source traffic data and road network static configuration information, and building a traffic flow parameter model, wherein the multi-source traffic data comprises internet road segment speed data, detector flow data and signal machine control scheme data, and the road networkstatic configuration information comprises road network space geographical position information, intersection number, road segment class, road segment length, road segment number, lane number and lane function; S2, analyzing road network congestion relevancy, and building a basic forecast group; S3, building a dual-stage attention mechanism-based depth learning traffic situation forecast model; and S4, building a traffic situation forecast system. The forecast accuracy and the transportability are relatively good.