In research and applications of large-scale comprehensive monitoring of crop growth conditions, the scientific determination of weights for multiple crop condition parameters is critical for improving monitoring accuracy. To evaluate the applicability of major weighting methods in integrated crop growth monitoring, the coefficient of variation method ( CV), entropy weight method ( EWM), principal component analysis (PCA), and the method based on the removal effects of criteria (MEREC) were used to construct the comprehensive crop growth index (CCGI) for maize. The constructed CCGI was then used for regional comprehensive monitoring of maize growth conditions and the accuracy of the crop growth monitoring was validated, thereby enabling the screening and evaluation of different weighting methods. The results showed that EWM and MEREC performed comparably in terms of the rationality of weight allocation for maize CCGI construction and the effectiveness of yield representation, and both outperformed CV and PCA. Regarding the monitoring accuracy of the CCGI constructed by using different weighting methods, EWM was slightly superior to MEREC, while both methods achieved higher monitoring accuracy than CV and PCA. Compared with ground observations of crop growth conditions, the average highest accuracy of comprehensive growth monitoring during the key growth stages (seedling stage, jointing stage, tasseling stage and filling stage) reached 92. 09%. Specifically, the average accuracies of regional maize growth monitoring during the key growth stages based on the CCGI constructed by using EWM, MEREC, CV, and PCA were 91.67%, 90.00%, 89.17% and 85.42%, respectively. The research result can provide a scientific basis for selecting appropriate weighting methods in the construction of CCGI and offer a reference for improving the accuracy of large-scale integrated crop growth monitoring.