How to improve the ship detection performance with limited annotation samples in a synthetic aperture radar （SAR） image has always been a hot spot in SAR image processing.In this paper，a multi-domain feature-guided unsupervised domain adaptation method is proposed.The knowledge is transferred from the annotated source domain （optical images） to the unannotated target domain （SAR images），and thus the dependency on the labeled SAR images is reduced.At the same time，the frequency domain transfer module，attention area-enhanced （AAE） module，and adaptive weighted module are designed to narrow the domain gap between the optical and SAR image domains，improve the efficiency of feature alignment between the source and target domains，and enhance the capability of feature transfer under challenging samples.Extensive experiments are carried out on public published datasets.The results show that the proposed modules are 10% better than the baseline，and the overall network outperforms other state-of-the-art （SOTA） methods.