Abstract: Based on the convolutional neural networks (CNNs) model, an image classification method of model fusing is proposed. The original data is composed of enhanced images and normalized data, and the mapping data is generated by negating original data. Then the CNNs models with the original data and the mapping data are trained separately. Next the two sets of CNNs models are fused to obtain the improved of CNNs model after training. The improved method is generalized to some more complex CNNs models after it is proved effective for simple cases through hypothesis, verification, and theoretical derivation steps. The experimental results show that the model after the fusion performs well. Compared with the original CNNs model, the classification accuracy the proposed model is increased by 1% and 3% based on the sets of CIFAR-10 and CIFAR-100 data, respectively.