The　mechanical　system　of　a　cable-stayed　bridge　with　a　steel　arch　tower　is　different　from　that　of　a　traditional　cable-stayed　bridge.　In　order　to　investigate　the　effects　of　ambient　temperature　variations　on　the　main　components　of　a　cable-stayed　bridge　with　a　tower　in　an　abnormal　shape,　an　actual　cable-stayed　bridge　with　a　steel　arch　tower　has　been　used　as　the　engineering　prototype.　The　online　temperature　data　of　the　onsite　environment　and　the　bridge　components　were　first　collected　and　used　to　analyze　the　time-varying　effects　of　the　environmental　temperature　on　the　cable　forces,　the　tower　obliquity　and　the　stress　of　the　main　girder.　Subsequently,　the　analysis　was　focused　on　the　cable　forces.　The　temperature　variation　simulation　was　applied　to　the　finite　element　model　of　the　bridge,　and　the　temperature　coupling　effects　caused　by　the　temperature　difference　between　different　bridge　components　on　the　cable　forces　were　analyzed.　Lastly,　the　temperatures　of　the　environment,　the　tower　and　the　main　girder　were　used　as　the　inputs,　while　the　cable　forces　were　defined　as　the　outputs　of　a　long　short-term　memory　neural　network.　The　network　was　trained　using　the　actual　measurement　samples　of　the　temperatures　and　the　cable　forces.　Data　compression　and　feature　extraction　were　realized　during　the　training　process.　Then,　the　prediction　model　for　the　cable　forces　was　established,　and　new　temperature　monitoring　data　were　input　into　the　network　model　for　predicting　the　cable　forces.　The　analysis　results　show　that　the　temperature　variations　of　the　main　girder　and　the　steel　arch　tower　follow　a　periodic　rule　and　lag　behind　the　ambient　temperature.　The　strain　variation　tendency　of　the　main　girder　accords　well　with　the　ambient　temperature,　but　the　latter　has　a　time　lag.　The　influence　of　the　ambient　temperature　variation　on　the　obliquity　of　the　arch　tower　is　very　small　without　any　periodic　rule.　A　linear　negative　correlation　is　found　between　the　cable　forces　and　the　ambient　temperature.　The　temperature　coupling　effect　caused　by　the　temperature　difference　between　different　bridge　components　should　be　considered　in　the　analysis.　The　long　and　short-term　memory　neural　network　is　suitable　for　the　data　with　timing　characteristics.　The　cable　force　prediction　model　based　on　the　neural　network　has　high　prediction　accuracy,　and　it　can　be　used　for　the　real-time　prediction　of　this　bridge.　©　2024　Chongqing　University.　All　rights　reserved.