Abstract: In order to solve the problem that convolutional neural network requires higher and higher memory and time efficiency, a new model for digital image classification is proposed in this paper. The model is a quantum convolutional neural network based on strongly entangled parameterized circuits. Firstly, the classical image is preprocessed and qubit-encoded, and the image feature information is prepared as a quantum state, which is used as the input of the quantum convolutional neural network model. The quantum convolutional layer, quantum pooling layer and quantum full connection layer of the model are designed to extract the main feature information efficiently. Finally, the Z-based measurement is performed on the model output, and the image classification is completed according to the expected value. In this work, the experimental data set is MNIST data. The classification accuracy of 0,1 and 2,7 classification reached 100%. The comparison experimental results show that the QCNN model of the three-layer network structure with average pooling downsampling has higher test accuracy.