Aiming at the poor detection effect of SSD and other algorithms on small ship targets in synthetic aperture radar (SAR) images and complex scenes, this paper proposes a method of ship detection based on a multiply connected feature pyramid network. First, according to the characteristics of small target ships in the image, a new feature extraction network I-VGGNet is constructed to solve the problem of the loss of feature information of small ships. Second, the multi-connection feature pyramid network module is added to strengthen the fusion of high-level semantic features of ships and low-level positioning features so as to improve the detection performance of the network for small and medium-sized ships. Finally, in order to solve the interference of complex scenes on ship target detection, this paper constructs a new loss function based on generalized intersection over union loss and focus loss to reduce the sensitivity of the network to the ship scale and accelerates the convergence of the model. The proposed method is tested in related experiments on the Chinese Academy of Sciences SAR image ship target data set. Experimental results show that the average accuracy reaches 94.79%, which is better than the existing mainstream detection algorithms. The frame rate reaches 22 frame/s, which meets the real-time detection requirements, the proposed method shows good adaptability to the detection of ship targets of different sizes in complex scenarios.