The　installation　of　large-scale　complex　Structural　Health　Monitoring　(SHM)　systems　causes　huge　amount　of　data.　Traditional　structural　analysis　and　data　processing　techniques　cannot　real-time　process　the　data　from　SHM　systems.　Timely　status　assessment　and　risk　warning　cannot　be　available　to　these　large-scale　complex　structures.　To　solve　this　problem,　parallel　improvement　is　conducted　in　the　traditional　multi-particle　swarm　coevolution　optimization　(MPSCO)　algorithm,　thus　a　new　PMPSCO　algorithm　based　on　cloud　computing　is　developed.　On　the　basis　of　the　PMPSCO　algorithm,　a　parallel　physical　parameter　identification　method　is　proposed　for　frame　structures,　and　a　numerical　experiment　of　a　15-story　frame　and　a　laboratory　test　of　a　7-story　steel　frame　are　analyzed　to　validate　the　proposed　method.　Furthermore,　the　relationship　between　speedup　ratio　and　number　of　parallel　nodes　are　explored　on　MATLAB　distributed　cloud　computing　platform.　The　identification　results　indicate　that　the　proposed　PMPSCO　algorithm　has　high　precision,　good　stability　and　expandability.　In　addition,　the　speed　of　algorithm　can　be　increased　greatly　by　adding　more　parallel　nodes　to　meet　the　needs　of　real-time　processing　monitoring　data.　©　2018,　Engineering　Mechanics　Press.　All　right　reserved.