As a vital part of the convolutional neural network model, the pooling model has the functions of dimension reduction and generalization of the model. In order to further improve the accuracy of the convolutional neural network model and optimize the learning performance of the model, this paper proposes an improved pooling model based on maximum pooling and average pooling, and the global handwritten digital datasets MNIST and CIFAR-10 data. The effectiveness of the improved pooling model was verified on the two dataset. Comparing with the common pooling model, it is found that the learning performance of the convolutional neural network with improved pooling model is better. In one iteration, the error rate decreases by 4.28% on the MNIST and decreases by 2.15% on CIFAR-10 datasets.