To improve the accuracy of sea fog recognition， a daytime sea fog recognition method is proposed by using a generative adversarial network with multi-scale feature fusion under the attention mechanism. The method first generated cloud images for the mid-infrared channel using a conditional generation adversarial network to eliminate the solar radiation effects of the original daytime mid-infrared channel cloud images， allowing the visible， far-infrared， and mid-infrared channel cloud images to be comprehensively utilized for sea fog monitoring. On these grounds， the pyramid split attention mechanism was introduced into the UNet network to improve the performance of data feature extraction for the three input channels. At the same time， multi-scale atrous spatial pyramid pooling was used in the transition layer of the codec to enhance the generalization ability of sea fog recognition at different scales by multi-scale feature fusion of multiple paths. Finally， the discriminant network was introduced to supervise the generation network to achieve an accurate definition of the edge of sea fog. The experimental results show that the accuracy of sea fog detection in this method is improved compared with the traditional method， the Probability Of Detection （POD） reaches 94.16 %， the False Alarm Ratio （FAR） is 11.61 %， and the Critical Success Index （CSI） is 83.59 %， which provides a new idea for daytime sea fog recognition.