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Load balancing of the cloud computing is one of the key issues of guaranteeing SLA agreement. Load Balancing for cloud computing, a load balancing strategy oriented SLA is put forward. Introduced artificial neural network thinking, this strategy establishes load balancing model. VMs load status is classified by single-layer Perceptron algorithm(SLPA), then BP neural network algorithm(BPNNA) combined with dynamic weighted round-robin algorithm (DWRRA) targetedly predicts and updates VMs load weight, finally, the task is dispatched to the feasible virtual machine according to the minimum weight. The CloudSim simulation experiment was performed, and the results show that the proposed strategy is feasible. Meanwhile, compared with the Weighted Least-Connection algorithm and particle swarm optimization algorithm, the strategy of this paper respectively reduce average response time by 43.6% and 22.5%, and reduce SLA violation rates by 20.7% and 14.4%. Therefore, the proposed strategy in response to user tasks, make response time short, SLA violation rate low, and SLA is guaranteed.