As　a　critical　component　of　the　transportation　system,　the　safety　of　bridges　is　directly　related　to　public　safety　and　the　smooth　flow　of　traffic.　This　study　addresses　the　aforementioned　issues　by　focusing　on　the　identification　of　bridge　structure　deterioration　and　the　updating　of　finite　element　models,　proposing　a　systematic　research　framework.　First,　this　study　presents　a　preprocessing　method　for　bridge　point　cloud　data　and　determines　the　parameter　ranges　for　key　algorithms　through　parameter　tuning.　Subsequently,　based　on　the　massive　point　cloud　data,　this　research　explores　and　optimizes　the　methods　for　identifying　bridge　cracks　and　spatial　deformations,　significantly　enhancing　the　accuracy　and　efficiency　of　identification.　On　this　basis,　the　particle　swarm　optimization　algorithm　is　employed　to　optimize　the　key　parameters　in　crack　detection,　ensuring　the　reliability　and　precision　of　the　algorithm.　Additionally,　the　study　summarizes　the　methods　for　detecting　bridge　structural　deformations　based　on　point　cloud　data　and　establishes　a　framework　for　updating　the　bridge　model.　Finally,　by　integrating　the　results　of　bridge　crack　and　deformation　detection　and　combining　Bayesian　model　correction　and　adaptive　nested　sampling　methods,　this　research　sets　up　the　process　for　updating　finite　element　model　parameters　and　applies　it　to　the　analysis　of　actual　bridge　point　cloud　data.