Aiming at the problems that the detection accuracy of ship detection in synthetic aperture radar (SAR) images is susceptible to speckle noise, and traditional algorithms only extract the underlying features and the generalization is poor, this paper proposes a ship detection algorithm based on deep convolutional neural network. First, the current single shot multibox detector (SSD) detection algorithm is applied to the field of SAR image ship detection, and its limitations are pointed out. Next, improved detection methods based on SSD are proposed, including context information fusion and transfer model learning. Finally, the experimental results on the open ship dataset show that, compared with the original SSD detection algorithm, the proposed method not only improves the target detection accuracy, but also ensures the efficiency of the algorithm.