Abstract: 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.