Abstract: The anatomical structure detection of fetal cardiac views is crucial for diagnosing fetal congenital heart disease. In practice, there is a large domain gap between different hospitals' data, such as the variable data quality due to differences in acquisition equipment.In addition, accurate annotation information provided by obstetrician experts is always very costly or even unavailable.This study explores the unsupervised domain adaptive fetal cardiac structure detection issue.Existing unsupervised domain adaptive object detection (UDAOD) approaches mainly focus on detecting objects in natural scenes, such as Foggy Cityscapes, where the structural relationships of natural scenes are uncertain. Unlike all previous UDAOD scenarios, we first collected a $\textbf{F}$etal $\textbf{C}$ardiac $\textbf{S}$tructure dataset from three hospital centers, called $\textbf{FCS}$, and proposed a multi-matching UDA approach ($M^3$-UDA}), including histogram $\textbf{M}$atching (HM), sub-structure $\textbf{M}$atching (SM), and global-structure $\textbf{M}$atching (GM), to better transfer the topological knowledge of anatomical structure for UDA detection in medical scenarios.HM mitigates the domain gap between the source and target caused by pixel transformation. SM fuses the different angle information of the sub-organ to obtain the local topological knowledge for bridging the domain gap of the internal sub-structure.GM is designed to align the global topological knowledge of the whole organ from the source and target domain.Extensive experiments on our collected FCS and CardiacUDA, and experimental results show that $M^3$-UDA outperforms existing UDAOD studies significantly. This proposed benchmark will open new opportunities for UDAOD.