The cover temperature has an important effect on the thermal behavior of the greenhouse. This research developed and validated a prediction model of the cover temperature considering dynamic cover absorptivity. The absorptivity of the cover changes with the time of day and depending on many parameters such as the refractive coefficient, extinction coefficient, and thickness of the cover. The dynamic absorptivity of the cover was used to improve the model's accuracy. The absorptivity of the cover was divided into the absorptivity of beam radiation, diffuse radiation, and ground-reflected radiation. This mathematical model also considered the thermodynamic exchanges between the cover and other components of the greenhouse including the convection, shortwave and longwave radiation. A computer program adopting the MATLAB standard solver ode45 was written to find a solution to the energy equations employing a fourth-order Runge–Kutta method. The input parameters of the model were the measurement of the meteorological environment and thermo-physical characteristics of the greenhouse components including those of the soil and inside air. The thermophysical characteristics of the greenhouse were determined by the material properties of the glass greenhouse and the construction scheme, which were not affected by the geographic location of the glass greenhouse. Initial input values for these equations were the measured temperatures of cover, soil, and air at t=0. Employing the computer program model built-in MATLAB, trends of temperature in the greenhouse were acquired by solving the unsteady-state energy balance equation for the structural components of the greenhouse and estimating heat absorbed by various surfaces. The model was validated utilizing measured data of three non-continuous periods of 10 days in three seasons in the north of China in Shandong province (36°08'N, 116°95'E). To predict the model accuracy, varying statistical indicators, including the root-mean-square error (RMSE), and the square of the correlation coefficient (R2) was determined from data series. The model’s accuracy was verified by comparing the calculated temperatures with experimental measurements for the glass greenhouse. The best results were obtained with RMSE=1.26 ℃ and R2=0.98 for the cover temperature. The worst results were obtained with RMSE=2.05 ℃ and R2=0.92 for the cover temperature. Statistical analysis confirmed that the developed model was effective in forecasting the microclimate of the greenhouse. Finally, we compared the accuracy of this model with related research abroad. With the comparison, we concluded that the accuracy of the model was higher than that of the related research abroad. Because this research considered the dynamic absorptivity of the greenhouse cover creatively. Besides, this study had an energy analysis of solar radiation flux absorbed by the cover with the experimental greenhouse as a study case. The results indicated that the south wall absorbed less solar radiation in the summer period, while other walls and roofs absorbed more solar radiation in the summer period. The solar radiation absorbed by the east wall and the west wall was almost equal. The north wall absorbed the least solar radiation compared with other walls and roofs. It is clear that the quantification of solar radiation as demonstrated here is of great interest to the growers and is essential for the model’s accuracy and greenhouse management.