This study is to test the technologies related to natural language processing (NLP) and text analysis to solve the problem of symptom analysis and symptomatic treatment for healthcare workers and patients after large-scale outbreaks of infectious diseases. Using the keyword extraction tool based on NLP technology combined with text analysis, the symptom description of the infected population obtained from the questionnaire survey was analyzed, and the efficacy of the symptomatic treatment drug was analyzed, and finally a pre-experimental system of online symptomatic treatment support drug selection system was produced. Through natural language processing (NLP) and text analysis of the symptoms of infected people, we found that high-frequency symptoms were mainly reflected in: nasal congestion, sore throat, fever and other high-frequency keywords, and through the analysis of alternative drug libraries, we also found that the drugs were mainly concentrated in: cold medicines, antipyretic drugs,and traditional Chinese medicines. In the early stage of infectious disease outbreak, it is difficult to use the existing drug library to make a prompt system under the premise of insufficient data collection, and a relatively complete symptomatic treatment support medication system can be formed after accumulating certain samples.