Abstract: Group-user access behavior in public map service has a social nature and there is a certain group-user access pattern, which has a high access aggregative and outburst feature. However, the feature has a great influence on the demands for cloud computing resources for public map service. Thus, how to effectively express and capture the access aggregative feature and the changes of access intensity over time, and predict the access load of public map service accurately, is the important key for selecting and scheduling cloud computing resources on demand, that can address the challenge of concurrent service for massive users. Based on the volume user access logs from public map service and the time-sequence clustering method, this paper first builds a time-sequence distribution model for group-user access arriving behavior; then using the features of multi-peak, variable and periodicity in access intensity, this paper optimally partitions the time-sequence of access arrival rate in a period into different temporal patterns; as there are different probability density distribution of access arrival rate in different temporal patterns, this paper proposes a method of service load forecasting method based on a smoothing time-sequence of cumulative probability distribution. This method has a low complexity and needs few priori data. Experimental results and method application prove that the optimal partition and prediction for the access arrival rate of group-user access based on a time-sequence have a good service response performance for massive users concurrent access, improve the utilization of cloud computing resource, and balance the service quality and cost in public map service.