The human visual system often focuses on the key features and structures of the target while weakening non-target areas when processing external information. In addition, in classical CNN models, noise in the image that propagates layer by layer may interfere with the representation of key information of the target, resulting in inaccurate feature extraction. Therefore, this article proposes an image classification method based on the dual fuzzy attention mechanism, named DFAM-CNN. Specifically, for the feature maps output by CNN convolutional layers, fuzzy channel attention mechanism and fuzzy spatial attention mechanism were first designed by introducing fuzzy logic technology. These two mechanisms were used to map and transform the feature maps along both the channel direction and spatial direction for generating important fuzzy feature maps that correspond to the original feature maps. Then, the channel weights of all feature maps and the weights of each element within each feature map were calculated based on all the determined important fuzzy feature maps, thereby highlighting the features related to the target in both the channel and spatial directions. Finally, dimensionality reduction was performed on the feature maps through fuzzy aggregation operations while retaining target-relevant features. To validate the effectiveness of DFAM-CNN, extensive experiments were conducted on both the public MedMNIST dataset and application-specific datasets. The experimental results validated the effectiveness of DFAM-CNN. Notably, compared with traditional max-pooling method, DFAM-CNN achieved accuracy improvements of 8.67% and 7.40% on the BreastMNIST and DermaMNIST subsets, respectively.