The　variations　of　cable　forces　directly　reflect　the　internal　mechanical　states　of　a　cable-tayed　bridge.　Therefore，the　moni‑　toring　of　stayed　cables　is　important　for　health　evaluation　of　the　cable-stayed　bridge.　However，most　of　the　existing　research　on　stayed　cables　focuses　on　force　identification.　The　prediction　of　future　forces　based　on　the　historical　data　is　still　difficult　to　achieve.　Therefore，this　study　proposes　a　cable　force　prediction　method　using　the　gated　recurrent　unit（GRU）neural　network.　The　applica‑　tion　framework　based　on　the　GRU　neural　network　is　established　by　using　the　processing　ability　of　the　GRU　neural　network　to　time　series　data，taking　into　account　the　strong　serialization　characteristics　of　cable　force　data.　The　network　construction　includes　the　in‑　put　layer，the　GRU　hidden　layer　and　the　output　layer.　The　cable　stress　time　history　data　of　a　real-world　cable-stayed　bridge　is　col‑　lected　as　the　training　and　validation　samples　for　the　GRU　neural　network.　Data　slices　and　normalization　are　applied　to　the　sampling　process.　The　GRU　neural　network　is　successfully　established　to　predict　future　cable　force　of　this　bridge.　The　Network　calculation　is　performed　using　a　gradient　descent　optimization　algorithm.　The　analysis　results　show　that　the　proposed　method　can　provide　satisfac‑　tory　predictions　for　cables　of　different　lengths.　©　2023　Nanjing　University　of　Aeronautics　an　Astronautics.　All　rights　reserved.