The classification of wafer defect patterns plays a crucial role in the wafer manufacturing process. Accurate identification of wafer defects enables the determination of the root causes of defects, thereby pinpointing issues in the production process.However, existing deep learning-based wafer defect classification methods are designed solely from the spatial or frequency domain, failing to achieve mutual supplementation and integration of spatial and frequency information. This limitation constrains the improvement of wafer defect classification accuracy. To address this issue, a dual-stream wafer defect classification network based on the fusion of spatial and frequency domain features, named SFWD-Net, is proposed.The network utilizes the proposed multi-scale feature extraction convolution module and multi-view attention module to form the spatial stream branch, which extracts spatial information from wafer images. The frequency stream branch, utilizing discrete wavelet transform, extracts frequency information from wafer images. After integrating spatial and frequency information, defect classification is performed. Experiments on the large-scale semiconductor wafer image dataset WM-811K demonstrate that SFWD-Net, by simultaneously designing the network from both spatial and frequency domains, achieves a classification accuracy of 99.299 2%, outperforming five other state-of-the-art methods and significantly improving the accuracy of wafer defect classification.