In order to reduce the volume and computational complexity of the convolutional neural network model without reducing the accuracy, a compression method of convolutional neural network model based on feature reuse unit called FR-unit (Feature-Reuse unit) was proposed. Firstly, different optimization methods were proposed for different types of convolution neural network structures. Then, after convoluting the input feature map, the input feature was combined with output feature. Finally, the combined feature was transferred to the next layer. Through the reuse of low-level features, the total number of extracted features would not change, so as to ensure that the accuracy of optimized network would not change. The experimental results on CIFAR10 dataset show that, the volume of Visual Geometry Group (VGG) model is reduced to 75.4% and the prediction time is reduced to 43.5% after optimization, the volume of Resnet model is reduced to 53.1% and the prediction time is reduced to 60.9% after optimization, without reducing the accuracy on the test set.