Aiming at the lack of adaptive target selection strategy for parameterized quantum circuits in current quantum convolutional neural network models, a quantum convolutional neural network model based on the particle swarm optimization algorithm is proposed to optimize circuits automatically. The model optimizes quantum circuits by encoding the quantum circuits as particles, then uses the particle swarm optimization algorithm to search for the circuit architectures that performs well in image classification tasks. Stimulation experiments based on Fashion MNIST and MNIST datasets show that the model has strong learning ability and good generalization performance, with accuracy up to 94.7% and 99.05%, respectively. Compared to current quantum convolutional neural network models, the average classification accuracy is improved by 4.14% and 1.43% to the maximum, respectively.