Research on the application technology of expert knowledge graph based on genetic search algorithm

Abstract The present study investigates the application of a genetic search algorithm on an expert knowledge graph. By employing binary coding for candidate experts in the expert database, the matching evaluation function is utilized to define the environmental fitness index, thereby optimizing the selection of expert groups. Experimental results demonstrate that the GES algorithm exhibits a superior matching degree and reduced time consumption when addressing science and technology item-matching requirements in review work. In comparison with the traditional BFS algorithm and GIS algorithm, the GES algorithm showcases enhanced potential for application and stability when confronted with large datasets. This paper further analyzes the impact of factors such as the total number of experts, number of fields, and number of scientific and technological projects on algorithm performance, confirming that adjustment of algorithm parameters can effectively enhance matching degree while reducing time consumption, thus providing technical support for practical implementation.