Abstract: Aiming at the problem of insufficient labeled data in remote sensing image detection of enteromorpha prolifera, this paper proposes a method for enteromorpha prolifera detection based on residual attention network (RANet) mutual guidance fusion learning. Firstly, this paper builds a RANet model with residual convolution module and attention mechanism for enteromorpha prolifera detection. Secondly, the two RANet models are used to guide each other under the dual network architecture, and the high confidence pseudo labels after the fusion of the double models are selected, and the training set is gradually expanded by combining data augmentation, so as to achieve high-precision enteromorpha prolifera detection by the iterative learning of the double models. The experimental results show that compared with threshold method, GAN, classical segmentation model (FCN, UNet, SegNet, PSPNet and DeepLabv3+), the detection method based on RANet mutual guidance fusion learning has higher detection accuracy. The model constructed in this study has feasibility for large-scale enteromorpha prolifera monitoring, which could provide technical support for the disaster monitoring of large-scale enteromorpha prolifera outbreak.