Underwater object detection often faces challenges such as complex environmental interference, unstable system performance, and low detection accuracy. To address these issues, this paper proposes WAD-YOLOv8, a lightweight object detection algorithm based on adaptive feature extraction and cross-scale feature fusion strategies. First, a context-aware residual feature extraction module (CCRF) is introduced in the backbone network, enabling the model to effectively integrate global and local information. Second, an adaptive down-sampling module (ADFE) guided by variable large kernel convolution attention mechanisms is employed to adjust sampling features dynamically, enhancing the network′s adaptability. Finally, the neck network is restructured by incorporating new cross-scale feature fusion connections, significantly improving the model′s robustness against environmental interference. Experimental results demonstrate that, compared to the baseline model, WAD-YOLOv8 achieves a 3.0% improvement in detection accuracy and a 2.6% increase in mAP50, while reducing model parameters and computation by substantial margins. The detection speed reaches 64 FPS, outperforming classical algorithms in both effectiveness and stability. These improvements highlight the model′s capability to address the challenges of underwater object detection, offering a highly efficient and reliable solution for complex underwater environments.