An underwater target detection algorithm that uses a multiscale and cross-spatial information aggregation network is proposed. First, a deformable layer aggregation module is used within the backbone network to extract features, enhancing the network's positioning accuracy. Second, the Conv2former module is used to enhance the neck's global information extraction capability and reduce missing detections caused by mutual occlusion among underwater targets. Finally, a multiscale attention parallel enhancement module that uses parallel convolution blocks to extract deeper features is proposed. This module integrates an efficient multiscale attention module to filter out interference from background and image distortion and introduces multiple cross-level connections to effectively integrate low-level local features with high-level strong semantic information, thereby improving model detection accuracy. The ablation experiment is conducted on the URPC dataset. Compared with the original model, the accuracy rate, recall rate, mean average precision (mAP)@0.5, and mAP@0.5∶0.95 of the improved model increase by 3.6 percentage points, 2.6 percentage points, 3.5 percentage points, and 3.3 percentage points, respectively. Tests on the RUOD dataset under different scenarios indicate that the proposed model offers notable advantages over several current mainstream models.