It is necessary to follow the running state of the servers for the virtual machine dynamical consolidation in cloud data centers, and the running state of the server can be affected by the load change of the cloud data center. Most of existing methods only concern the CPU utilization change of the current server. We propose a CPU utilization prediction model based on the Kalman filtering, formulate a load variation model of the cloud data center based on the variation coefficient of the CPU utilization of all servers, and then describe the prediction procedure based on the Kalman filtering in detail. Moreover, the energy consumption and performance evaluation of the cloud data center are discussed. Finally, we conduct experiments on the CloudSim with five workloads of PlanetLab. Experimental results show that the Kalman filtering can better reflect the change tendency of the CPU utilization, and maintain better computational performance with lower energy consumption.