Lodging stress is one of the main disasters in maize production, which seriously affects the yield and quality of maize and mechanical harvesting ability. It is the basis of remote sensing monitoring of maize large-scale lodging disasters to analyze the changes of maize canopy structure and spectral response characteristics under different lodging stress. Stem lodging, stem fold and root lodging were set up in the tasseling stage and the middle filling period. Based on the field continuous observation experiments, the effects of growth stages and lodging types on dynamic changes of canopy structure and self-recovery ability of maize were analyzed. Hyperspectral data of lodging maize canopy were processed by the traditional spectral transformation and continuous wavelet transform. Leaf area density (LAD) was taken as the index of the lodging maize canopy structure characteristics. The best sensitive bands and wavelet coefficients of leaf area density were selected. The hyperspectral response model of leaf area density was constructed based on random forest method, andthe accuracy of the model was verified by the measured samples which were not involvedin modeling. Focus on the influence of wavelet decomposition scale and spectral resolution on LAD spectral response ability. The results showed that leaf area density, as a canopy structure indicator of total leaf area per unit volume, had a good response relationship with lodging stress intensity. The LAD of lodging maize in the filling stage was generally higher than that in the tasseling stage. The LAD of the tasseling stage was as follows: stem fold>root lodging>stem lodging>no lodging. The LAD of the filling stage was as follows: root lodging>stem fold>stem lodging>no lodging. After continuous wavelet transform, the response ability of maize lodging canopy spectrum to leaf area density is generally better than that of traditional spectral transform. The response ability of lodging maize canopy spectrum to leaf area density after continuous wavelet transform is generally better than that of traditional spectral transform. As the wavelet decomposition scale increases, the correlation between LAD and sensitive bands is stronger, and the correlation coefficient of 10 scale is the highest, reaching 0.74. The accuracy of the model constructed by continuous wavelet transform is generally better than that of traditional spectral transform. The model constructed by the original spectral wavelet transform has the highest precision, the R2 of test sample is 0.811, and the RMSE is 1.763. It showed that continuous wavelet transform technology can highlight and utilize the subtle information in the canopy spectra. Therefore, leaf area density can effectively quantify the variation characteristics ofmaize canopy structure under different lodging stress. Continuous wavelet transform can effectively improve the response of the canopy spectrum to the structural parameters of the lodging maize. The model of lodging maize leaf area density based on random forest method has high accuracy and stability, which can provide prior knowledge for remote sensing monitoring of summer maize lodging disaster at regional scale.