In the medical field, with the development of 5G technology, the widespread implementation of various collection devices has led to the accumulation of a large amount of multi-modal data, which includes images, text, audio, and video. This rich multi-modal data is effectively utilized in various scenarios. For instance, image data can assist in the early diagnosis of cancer, whereas text data is applied to medical record analysis and disease classification. However, the heterogeneous nature and large volume of multi-modal medical data pose higher demands on data management systems, making the effective management and utilization of these data a significant research area. Traditional data warehouse systems, while capable of handling basic data storage and querying needs, often fall short when confronted with the heterogeneity, immense scale, and complexity of data integration and analysis tasks in multi-modal medical data. To effectively manage this data and deepen its analysis, this paper proposes a data lake based multi-modal medical data management platform. The platform is designed to overcome the limitations of traditional data warehouses when handling large-scale, multi-modal medical data, using a flexible and scalable approach. It can integrate various types of medical data, enabling users to conduct analyses tailored to specific medical applications.