Node priority is often used to evaluate the performance of heterogeneous cluster nodes, and it is of great importance to provide suitable weight for each priority evaluation index. The AHP (analytic hierarchy process) was chosen to establish the evaluation index system of node priority, and the initial weight of each index was calculated. The BP (back propagation) neural network was then used to optimize the weights obtained by using AHP. The input of the BP neural network was the node′s performance index values collected during execution of cluster, and the output was the corresponding priority of the node. After the network training, the weight matrix was obtained and used to calculate the optimized weights. The experimental results show that the cluster node priority evaluation model based on AHP and BP can evaluate the node performance more accurately. Compared with the default resource allocation algorithm of Spark and the comparison algorithm with unoptimized weights, the cluster performance is improved effectively by using the node priority optimized. When running the same kind of load with different amount of data, the average cluster performance increases by 16.64% and 9.76%, respectively; and when running different loads with the same amount of data, the average performance of the cluster increases by 12.49% and 6.54%, respectively.