Driven by the strategic decision of peak carbon and carbon neutrality, wind energy has become one of the research objectives of many scholars for its clean, green and renewable advantages. The main application of wind energy is wind power generation, in which the wind turbine blade is an important component to establish a link between wind energy and wind power generation, so it is very important to monitor the health state of the blade and defect identification. In this paper, for the wind turbine blade internal hole defects, the use of finite element analysis method to construct the internal defect model of the wind turbine blade, after simulation, through the Steady-state and transient thermal analyses, found that the defect buried depth is the main influence factor of the defect recognition speed, The defect size and embedding depth of the blade surface containing defects have more influence on the maximum temperature, the depth of defects containing defects have a smaller impact on the surface of the blade with the maximum temperature.