Equipment that operates in harsh and variable underwater environments is essential for conducting underwater research and development. The current underwater target detection models are too large in parameter count and computation, which limits the deployment of underwater equipment with limited resources. In order to solve the problem of excessive parameter count and computational volume of underwater detection models, a lightweight underwater target detection model RCE-YOLO is proposed.Firstly, the spatial attention weights of RFAConv are utilized to improve the ability of CBS to process the information in the receptive domain and to enhance the ability of C2f to fuse spatial feature information, so as to enhance the model's ability of detecting small and dense targets. Second, the CCFM is fused with the Dysample module, which is able to utilize the different scale information more effectively and reduce the blurring and distortion produced by the original sampling through the internal point sampling method. Finally, the Efficient multi-scale attention mechanism is fused in the SPPF forward propagation process, which makes the model focus on the key information of the underwater target and reduces the false detection rate and misdetection rate. The experimental results show that the improved lightweight model is validated on the dataset DUO, and the mAP50 and mAP50:90 values reach 83.6% and 64.2%, respectively, which are 1.4% and 1.2% higher compared to the mAP50 and mAP50:90 values of the benchmark model of YOLOv8, and the number of parameters and the amount of computation drop by 32.3% and 0.9G, respectively. compared to other The target detection model meets the needs of underwater target detection in harsh and variable environments, and lays the foundation for lightweight deployment of underwater equipment.