The modeling of uncertainty refers to transforming uncertain factors into mathematical models so that they can be analyzed and calculated. In the modeling process, it is necessary to convert uncertainty factors into probability distributions and estimate the parameters of the distribution. Commonly used probability distributions include normal distribution, Poisson distribution, exponential distribution, etc. It is also necessary to model the relationship between uncertain factors in order to analyze complex uncertain systems. Common methods include Bayesian networks, Markov chains, etc. Uncertainty modeling and computation have extensive applications in many fields. In the financial field, uncertainty modeling and calculation can be used to predict and control stock prices. In the field of medicine, uncertainty modeling and computation can be used to make decisions about the diagnosis and treatment of patients. In the field of natural disasters and environmental management, uncertainty modeling and calculation can be used to predict and respond to the risks of natural disasters. In the fields of machine learning and artificial intelligence, uncertainty modeling and computation also have important application value.