Under the background of large amount of data support, how to use a large number of SAR images efficiently and improve the accuracy of ship target detection is the current problem of ship target detection. This paper focuses on how to improve the accuracy of YOLOv4 algorithm for SAR ship target detection, and presents a YOLOv4 enhancement algorithm that combines multiscale and attention enhancement. The Attention Module (CBAM) is added to the PANet of the original YOLOv4, and the enhanced K-means clustering algorithm is used to cluster the ship target real frame in the dataset, and the result of the anchor frame is transformed linearly to make the algorithm anchor frame more suitable for the training set. Experiments show that the average accuracy () of the proposed algorithm in SAR ship detection is 94.05%, which is 0.7% higher than that of the original YOLOv4. The experimental results fully demonstrate that the proposed algorithm can improve the accuracy of SAR ship image detection and provide technical support for the accuracy of sea activities judgment.