Abstract: To address the issues that traditional models cannot effectively handle the complex underwater environmental noise, the target scale varies greatly, and the inability to balance model size and accuracy, MSCA-UODA(Multi-scale Context-Aware Underwater Object Detection Algorithm) model was proposed. The model designs a context-enhanced downsampling module, CEADown (Context Enhanced ADown), which can effectively reduce the model parameters, capture context information efficiently and reduce underwater environmental noise. This model also proposes a multi-scale feature extraction module based on dual-path partial connection, named CSP-MSPF(Cross Stage Partial-multi-scale Partial Feature), and uses SHSA (Single-Head Self-Attention) mechanism to improve C2PSA, enhancing the multi-scale feature extraction capability of the model. Through experiments, MSCA-UODA improved by 2.0% and 1.1% respectively on mAP50 compared with baseline model on the datasets URPC2020 and DUO. The number of parameters decreased by 12.01%, and its comprehensive performance was superior to the current mainstream object detection models.