As an essential part of big data governance applications, data analysis is characterized by time-consuming and large hardware requirements, making it essential to optimize its execution efficiency. Earlier, data analysts could execute analysis algorithms using traditional matrix computation tools. However, with the explosive growth of data volume, the traditional tools can no longer meet the performance requirements of applications. Hence, distributed matrix computation systems for big data analysis have emerged. This study reviews the progress of distributed matrix computation systems from technical and system perspectives. First, this study analyzes the challenges faced by distributed matrix computation systems in four dimensions:programming interface, compilation optimization, execution engine, and data storage, from the perspective of the mature data management field. Second, this study discusses and summarizes the technologies in each of these four dimensions. Finally, the study investigates the future research and development directions of distributed matrix computation systems.