Major sudden infectious diseases are often characterized by high infectivity， rapid mutation and significant risk， which pose substantial threats to human life security and economic development. Epidemiological investigation is a crucial step in curbing the spread of infectious diseases and are prerequisites for implementing precise full-chain infection prevention and control measures. Existing epidemiological investigation systems have many shortcomings， such as manual inefficiencies， poor data quality， and lack of specialized knowledge. To address these defects， a set of technological application schemes were proposed to assist in epidemiological investigation based on the existing digitization combined with knowledge graph. Firstly， a knowledge graph was constructed on the basis of the five categories of entities： people， locations， events， items， and organizations， as well as their relationships and attributes. Secondly， following the idea of identifying risk points and tracing to close contacts based on cases， cases were used as the starting point with points as the focuses to aid in determining at-risk populations and points risk. Finally， through the visual analysis of epidemiological investigation data， several applications were implemented， including information placement in epidemiological investigation， tracing of the spread and propagation， and the awareness of epidemic situations， so as to assist in the successful implementation of major sudden infectious disease prevention and control work. Within the same error range， the accuracy of the graph enhancement-based trajectory placement method is significantly higher than that of the traditional manual inquiry-based method， with the determination accuracy within one kilometer reached 85.15%； the graph enhancement-based method for determining risk points and populations improves the efficiency significantly， reducing the average time to generate reports to within 1 h. Experimental results demonstrate that the proposed scheme integrates the technical advantages of knowledge graph effectively， improves the scientific nature and effectiveness of precise epidemic prevention and control strategy formulation， and provides important reference value for practical exploration in the field of infectious disease prevention.