Abstract: Abstract: Evolutionary algorithms have been widely used in the field of crop model parameter calibration. Since the crop model structure includes multiple sets of ordinary differential equations with nonlinear, discontinuous and multi-peak characteristics, it is especially important to select appropriate evolutionary algorithms based on crop model characteristics. At the same time, the parameters of the evolutionary algorithm have a great influence on the performance of the algorithm. These parameter selections are often based on experience, which increases the uncertainty of the optimization algorithm in the model parameters calibration process . This article was targeted on problem of selection and parameter uncertainty of the evolutionary algorithm applied to the crop phenological model parameter correction quasi-process . In this paper, the rice RiceGrow phenological predicting model was applied to compare the correction accuracy, convergence speed and stability robustness of 3-tyepe evolutionary algorithms in application. Comparison algorithms included differential evolution series algorithms (standard differential evolution algorithm(DE) and adaptive control parameters modified differential evolution algorithm(ACPMDE)), co-evolutionary genetic algorithm series (individual advantage genetic algorithm, M-elite co-evolution algorithm) and particle swarm optimization series (standard particle swarm optimization, multi-subgroup particle swarm optimization based on autonomous learning and elite groups). Using the multi-year field experiment data of 5 species of Wuyujing and Xuehuanian in different ecological points such as Yixing, Xinghua in Jiangsu province and Gaoyao in Guangdong province, the accuracy, convergence rate and stability robustness of the automatic correction of the model parameters was quantitatively analyzed with different evolutionary algorithms. The results showed that: 1) the correction accuracy of the model parameters of the model with adaptive control parameters modified differential evolution algorithm was higher than other algorithms, and the parameters of the algorithm were easier to determine. The cross-probability factor and scaling factor of the algorithm were adaptively adjusted with the individual fitness function during the evolution process, in which the dependence of the standard DE algorithm parameters on the optimization problem was avoided and the robustness of the algorithm was improved. The RMSE(root mean square error) between the predicted and observed values of jointing, heading and maturity stage was 1.7-4.6 days; The normalized root mean spuare error was 1.8%-5.8%; MAD(mean absolute difference) was 1.4-3.3 days, and determination coefficient R2 was 0.977-0.997, which was 0.634 days, 0.608%,0.453 days, 0.09% smaller than co-evolutionary genetic algorithm series, 1.399 days, 1.35%, 1.039 days,0.23% smaller than PSO series. 2) Applying adaptive control parameters to improve the differential evolution algorithm showed good convergence speed and stable robustness on the phenomenological model parameter correction. The standard deviation of the objective function value of 100 times repeated calibration experiment approached 0, and the standard deviation of the variety parameter values obtained by each correction was also smaller than other algorithms. With the same accuracy, the adaptive control parameter modified differential evolution algorithm converges 117 iterations faster than the standard differential evolution algorithm. The research showed that the automatic correction quasi-method of crop phenological model parameters based on adaptive control parameters modified differential evolution algorithm had good accuracy and stability and was suitable for practical application.