Encountering the trend of massive amd multidimensional data, in order to improve the quality of clustering algorithm and solve the computing resources bottleneck of traditional clustering algorithm when clustering massive amounts of high dimensional data, this paper proposes a Parallel Quantum-Behaved Particle Swarm Optimization Fuzzy C-Means clustering algorithm based on cloud computing for power load curve clustering. Quantum particle swarm intelligence algorithm (QPSO) is introduced into the traditional Fuzzy C-Means (FCM) clustering algorithm, QPSO‘s stronger global search ability is used to overcome FCM algorithm‘s weakness of falling into local optimum easily and being sensitive to initial clustering center. Cloud computing is adopted in the MapReduce programming framework and HBase distributed database to parallelization algorithm is improved. Many experiments verify that compared with traditional FCM algorithm and AFCM algorithm the clustering accuracy is increased by about 10% with better parallel performance.