The Impact of a Knowledge Discovery-Based Psychoanalytic Intervention in the Treatment of Tuberculosis in University Students with Different Doses of Isoniazid

Tuberculosis (TB) is an infectious disease that poses a serious threat to the health of the population in China, and TB outbreaks in universities have aroused great concern in society. Psychological emotions have a large impact on the academic lives of university students, and nowadays it is not only labour-intensive but also slow to monitor and analyse and deal with the psychology of university students' daily lives in a uniform manner. If psychological problems are not detected and given feedback in a timely manner, they can have a series of negative effects on the individual university student. In this paper, we apply the Bi-LSTM model and the CNN model neural network algorithm to learn the text data, and finally have 95.55% and 90.03% accuracy in the sentiment analysis experiment, respectively, which provides a feasible solution to solve the batch rapid analysis of the psychological changes reflected in the daily text of university students. Risk communication for TB emergencies should emphasize public participation, timely release of information about the epidemic, and good monitoring of public opinion.