Due to the insufficient underwater salient object detection datasets， the underwater image salient object detection network based on deep learning is prone to overfitting， which affects the performance of the network. In response to the above problems， this paper introduces an image style conversion method and proposes an underwater salient object detection network based on CycleGAN. The network generator is composed of an image style conversion subnetwork and a salient object detection subnetwork. First， the network trains the domain transform subnetwork through unsupervised cascade method， and uses the network to preform style transform on in-air and underwater images to construct training and testing datasets， so as to solve the insufficient problem of underwater salient object detection. Then， it uses in-air and salient object detection datasets after style transformation to train the salient object detection subnetwork to enhance the feature extraction ability of the network. Finally， the output results of the two image styles are fused and optimized to improve the performance of the saliency detection network. Experimental results show that compared with the land and underwater salient object detection network， the mean average error （MAE） and F-measure are relatively increased at least 10.4% and 2.4%， respectively.