Deep learning algorithms based on convolutional neural networks are attracting attention in the field of image processing. To improve the accuracy of the feature extraction process and the convergence rate of parameters

as well as optimize the learning performance of the network

an improved dynamic adaptive pooling algorithm is proposed

which compares the effect of different pooling models on learning performance. A convolutional neural network model

which is trained with different pooling models

is constructed. The results of the trained model are verified in different iterations. To compensate for low accuracy and slow convergence speed

a dynamic adaptive pooling model is proposed

which trains the network with different pooling models. The effect of the model on the accuracy and convergence rate in different iterations are then studied. Contrast experiment shows that the dynamic pooling model has optimal learning performance. The maximum improvement of the convergence rate on handwritten database is 18.55% and the maximum decrement of the accuracy rate is 20%. A dynamic adaptive pooling algorithm can improve the accuracy of feature extraction

convergence rate

and accuracy of the convolutional neural network

thereby optimizing network learning performance. The dynamic adaptive pooling model can be further extended to other deep learning algorithms related to convolutional neural networks.