Traditional convolutional neural network uses pooling layer to reduce the dimension of feature

which usually results in information loss

thus affecting the expression ability of the network. To solve this problem

the parameterized pooling layer is used to replace the pooling layer in the conventional convolutional neural network

and the parameterized pooling CNN (PPCNN) is proposed. In the case that only a few network parameters are added in the parameter pooling layer

it is possible to retain the desired features. At the same time

the forward propagation information of the pooling layer is added

which affects the update of weight in the backpropagation algorithm

and the network convergence speed is faster. Compared with the conventional convolutional neural network model and some improved models

experimental results show that the PPCNN model is effective.