## 1 Introduction  
The development of information technology continues to promote the transformation of Internet technology, and Web technology, as the iconic technology of the Internet era, is at the core of this transformation. Human beings have experienced the Web 1.0 era, characterized by document interconnection, and the Web 2.0 era, characterized by data interconnection, and are moving towards a new Web 3.0 era based on knowledge interconnection [1]. The purpose of knowledge interconnection is to build a World Wide Web that both humans and computers can understand, making the network more intelligent. In order to represent the whole interconnected human cognitive world more profoundly, a new method of knowledge representation and management has been developed: the knowledge graph. Knowledge graphs are the foundation and bridge for the realization of intelligent semantic retrieval, laying the foundation for knowledge interconnection in the World Wide Web. The concept of the knowledge graph was formally proposed by Google in May 2012 and its development continued after 2013 with the continuous progress in intelligent information services and applications. Today, knowledge graphs are not only a research hotspot in academia but also an indispensable application technology for intelligent services in industry. Medicine is one of the vertical fields where knowledge graphs are most widely used, and it is also a hotspot of research for the field of artificial intelligence in China and abroad, including research in disease risk assessment, intelligent assisted diagnosis and treatment,medical quality control, and medical knowledge questions and answers. In addition, many companies have built their own knowledge graphs, such as the [PERSON] medical think tank at Ali Health ([[https://www.mdeer.com](https://www.mdeer.com)]([https://www.mdeer.com](https://www.mdeer.com)), accessed on 7 December 2022), the AI medical knowledge graph Artificial Intelligence + Professional + GC (APGC) developed by Sougo, and other applications. In the medical field, typical medical knowledge maps include the Systematized Nomenclature of Medicine--Clinical Terms (SNOMED-CT) ([[https://www.snomed.org](https://www.snomed.org)]([https://www.snomed.org](https://www.snomed.org)), accessed on 8 January 2023), Watson Health ([[https://www.ibm.com/watson](https://www.ibm.com/watson)]([https://www.ibm.com/watson](https://www.ibm.com/watson)), accessed on 8 January 2023) developed by International Business Machines (IBM) Corporation, and the Chinese medicine knowledge map developed by Shanghai Shuguang Hospital in China. With the ongoing development of medical informatization, a huge amount of medical data has been generated. In the face of massive semi-structured and unstructured medical resources, quickly discovering relevant information useful to users is a major challenge. Moreover, the recent research on the construction of medical knowledge graphs has focused on a single source of medical data, which cannot meet the actual needs of users well. Because of the above issues, we propose a method for the construction of DIK medical knowledge graphs with multiple data sources in this paper. We first used different methods to collect data from different medical data sources, and this part of the work was described in a previous publication [2]. Among the sources used, the BiLSTM-CRF model and Web crawler data extraction were used for Chinese electronic medical records and online medical communities, respectively. Secondly, in the data classification stage, to give full play to the values of the different medical data sources, a data source selection strategy was developed, as described in this paper. This strategy was used to divide the data categories according to the characteristics of different medical data sources in order to make the best use of the medical data.