To achieve precise detection of underwater biological targets and support the sustainable development of marine resources ,this paper proposes an improved YOLO11n-based detection algorithm. Building upon the YOLO11n baseline model, the algorithm introduces the PPA ( Parallel Patch Attention ) module to enhance feature extraction capability for small underwater targets; employs the Detect_ Efficient module to optimize the detection head and improve multi - scale target detection accuracy; and incorporates the CSFCN feature calibration module to address feature loss caused by the lack of global contextual information during convolution,thereby boosting detection accuracy in blurred underwater images. Compared with the original YOLO11n,the improved model achieves a 1. 9% increase in mAP @ 0. 5 for underwater target detection. When compared to mainstream object detection algorithms, the proposed model also demonstrates superior performance in both precision and recall,reaching 85. 6% and 75. 5% ,respectively. Experiments verify that the improved YOLO11n exhibits better detection performance in underwater target detection tasks compared to mainstream models.