When modeling and fitting various kinds of epidemic outbreaks, the value of parameters has always been an important practical problem for many scholars. In the existing studies, most of the authors select a fixed parameter by referring to the relevant literature or combined with medical experiments. With the help of Euler difference transformation and the characteristics of the solution of linear equations, we innovatively propose a dynamic update strategy of epidemic diffusion parameters based on data-driven in this study in order to overcome the above limitation. The method can help decision-makers to calculate the optimal parameters of epidemic spread by combining the real-time update data. A case study is conducted with the COVID-19 data of Wuhan. The results show that the dynamic parameter update strategy designed in this paper can effectively improve the accuracy of the evolution prediction of epidemic outbreaks, which provides an important decision support for the accurate allocation of government emergency resources.