Retraction Note: Research on image classification method based on convolutional neural network

Image classification method is currently the more popular image technology, but it still has certain problems in practice. In order to improve the image classification effect, this study proposes a new convolution kernel, which can effectively detect the corresponding features with different transformations by actively transforming the relative positions of the connections in the convolution kernel. Moreover, in a network, replacing a traditional convolution kernel with a complex convolution kernel can significantly improve network performance. In order to verify the performance of the image classification method proposed in this study, the performance comparison of the algorithm was performed by setting a control experiment. The research results show that the proposed method has certain effects and can provide theoretical reference for subsequent related research.