Abstract: Abstract: Accurate estimation of crop coefficient is critical to predicting actual crop evapotranspiration and developing accurate irrigation schedules. In order to reflect the daily crop coefficient changes of the crops, this study comprehensively considered the common influence of meteorological and biological factors on crop growth, a meteorological-physiological double function multiplication model was established based on geothermal temperature and leaf area index by using the evapotranspiration data and meteorological data of summer maize of lysimeter of the Wudaogou Hydrological Experimental Station. Combining with the gradient descent method, a meteorological-physiological double function multiplication model was constructed to estimate crop coefficients and optimize the accuracy of the model. The results showed that the model calculated the crop coefficient of summer maize. The model could also be used for the estimation of the evapotranspiration of summer maize with higher precision. In different samples, including training samples and test samples, and the entire growth period of summer maize, the precision calculated by the model was high. In the entire growth period of summer maize, between the measured value and the calculated value, the average absolute error was only 0.12. In addition, the root meant square error was 0.15. And the correlation was 0.91 between the measured value and the calculated value on the crop coefficient of summer maize. Regarding the estimation of evapotranspiration, between the measured value and the calculated value, the average absolute error was only 1.0 mm/d, and the root meant square error was 4.5 mm/d. In addition, the correlation was 0.75 between the measured value and the calculated value on the evapotranspiration of summer maize. And by using the crop coefficient of the Food and Agriculture Organization of the United Nations (FAO) which was recommended, the evapotranspiration of summer maize was calculated. And the model constructed in this study was compared with the calculated results, it was found that the evapotranspiration accuracy calculated by meteorological-physiological double function multiplication model was increased by more than 3 times when the error between measured value. The calculated value was within 2 mm/d and 3 mm/d. And the error was almost all within 4 mm/d. In this study, the meteorological-physiological double function multiplication model based on ground temperature and leaf area index could be more accurately used for crop coefficient and actual evapotranspiration calculation of summer maize. And the research incorporated a machine learning algorithm (a multi-disciplinary interdisciplinary) to further improve the accuracy of crop coefficient calculation. The construction of an accurate crop coefficient model had great significance for the accurate prediction of evapotranspiration and further for the development of accurate irrigation plans. The model simplified the calculation of summer corn crop coefficients, clarified the comprehensive influence degree of ground temperature and leaf area index on crop coefficients, improved the calculation accuracy, and could be used for the dynamic calculation of corn crop coefficients. The evapotranspiration data measured by a large-scale weighing lysimeter at the Wudaogou Hydrological Experimental Station was used to calculate the actual crop coefficient in combination with a series of meteorological data. The crop coefficient and leaf area index were fitted using the Michaelis-Menten equation to obtain the influence of biological factors. The multi-factor regression was used to select the meteorological factor-ground temperature (0 cm), which had the closest effect on the crop coefficient, and performed exponential fitting. Finally, the gradient descent method was used to optimize the model parameters, and the measured results were used to evaluate the calculation results. It had great significance to grasp the dynamic change characteristics of crop coefficients during the summer maize growth period and accurately estimate the actual evapotranspiration of crops.