Given　the　intricate　operating　environment　of　steel　truss　bridges,　data　anomalies　are　frequently　initiated　by　faults　in　the　sensor　monitoring　system　itself　during　the　monitoring　process.　This　paper　utilizes　a　steel　truss　bridge　as　a　case　study　in　engineering,　with　a　primary　focus　on　the　deflection　of　the　main　girder.　The　paper　establishes　an　Attention　Mechanism-based　Bidirectional　Long　Short-Term　Memory　Neural　Network　(ATT-BiLSTM)　model,　with　the　objective　of　accurately　repairing　abnormal　monitoring　data.　Firstly,　correlation　heat　maps　and　Gray　correlation　are　employed　to　detect　anomalies　in　key　measurement　point　data.　Subsequently,　the　ATT-BiLSTM　and　Support　Vector　Machine　(SVR)　models　are　established　to　repair　the　anomalous　monitoring　data.　Finally,　various　evaluation　indexes,　including　Pearson’s　correlation　coefficient,　mean　squared　error,　and　coefficient　of　determination,　are　utilized　to　validate　the　repairing　accuracy　of　the　ATT-BiLSTM　model.　The　findings　indicate　that　the　repair　efficacy　of　ATT-BiLSTM　on　anomalous　data　surpasses　that　of　SVR.　The　repaired　data　exhibited　a　tendency　to　decrease　in　amplitude　at　the　anomalous　position,　while　maintaining　the　prominence　of　the　data　at　abrupt　deflection　change　points,　thereby　preserving　the　characteristics　of　the　data.　The　repair　rate　of　anomalous　data　attained　93.88%,　and　the　mean　square　error　of　the　actual　complete　data　was　only　0.0226,　leading　to　substantial　enhancement　in　the　integrity　and　reliability　of　the　data.　©　2025　by　the　authors.