Convolutional neural network has achieved a lot of success in the field of computer vision in the recent years. With the rapid development of convolutional neural network, most image classification task has achieved significant performance improvement. Although some progress has been made in the research of image classification methods, there are still some deficiencies. For example, many existing methods are difficult to adaptively mine the feature importance within the sample and the feature correlation between sample scales. In order to solve the above shortcomings, this paper mainly studies image classification based on dynamic adaptive learning. In this paper, Multi-Scale Dynamic Convolution (MSDC) is proposed and verified on the standard image classification data set. Our method can be adjusted adaptively according to different scales of input data. The experimental results show that the proposed method exceeds the relevant comparison methods.