Histopathological image analysis is the “gold standard” for cancer diagnosis, which plays an important role in the prognosis and treatment of patients. Currently, in the field of AI medical imaging, the classification of pathological images based on Convolutional Neural Network (CNN) has become a research hotspot. However, the Max/Average pooling module is widely used in traditional CNNs, which inevitably lose massive feature information in pathological images, resulting in low classification accuracy and difficult model convergence. Therefore, this study proposes a pathological image classification method based on Wavelet Decomposition Convolutional Neural Network (WDCNN). This method can make the traditional CNN model learn the frequency domain information. It introduces the multi-scale analysis of wavelet transform into a CNN model and uses wavelet decomposition to replace the traditional pooling layer, which reduces the loss of features compared with max and average pooling. In view of different characteristics of the space domain and the frequency domain, the high-frequency components after wavelet decomposition are added to the next layer through shortcut connections to make up for the detailed feature information lost in the pooling process. This paper evaluates the performance of different pooling methods and different wavelet basis functions in pathological image classification on the Camelyon16 dataset. According to the experimental results, the CNN model integrated with wavelet decomposition can improve the classification accuracy of the network.