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data center is an important part of enterprise informatization, and the core idea of cloud computing is to integrate data center into a resource pool, and to conduct unified scheduling and management of resource pools. With the development of virtualization technology, the current resource utilization of the data center is higher and higher, but there are still a lot of waste of resources situation, the reason for this is that the current load forecasting algorithm for data center in the future also has certain limitation, if the load forecast for the future is far greater than the actual load, leading to a large number of the virtual machine resources utilization rate is not high, the opposite can lead to increase consumption of resource utilization, the virtual machine between different physical servers in the cloud platform of load imbalance, part of the physical server load is too large, lead to cloud computing platform response time is too long. Therefore, it is more and more important to select a suitable load prediction algorithm for cloud computing platform. How to balance the above problems is a key research direction in cloud computing. Based on the static coefficients of the algorithm, a dynamic coefficient extraction method is designed. By equidistant method, the static coefficients are divided into several parts for training, and then the corresponding coefficients of the minimum error of the time period are extracted in the prediction process. After the prediction is over, the error is recalculated and the old error is covered by means of means. The experimental results show that the prediction error is significantly less than the prediction error of the static coefficient based on the adaptive cubic index smoothing algorithm, and the computational complexity is low, which has certain application value.