The analysis of canopy spectral response mechanism of crop lodging stress is an important basis for remote sensing monitoring of large-scale crop lodging disasters. Lodging stress directly change the ratio of visual stem, leaf and panicle in remote sensing spectrum detection field of view. By analyzing the relationship between canopy spectra and the ratio of visual stem, leaf and panicle, this paper explores the change regulation of visual stem, leaf and panicle components and spectral response of rice canopy under different intensities of lodging stress, and provides theoretical support for remote sensing monitoring of large-scale crop lodging disaster. Taking the real lodging rice in Xinghua City and Dafeng District of Jiangsu Province in 2017 as the research object, with the support of field observation experiment , the rule of canopy spectral variation of lodging rice with different lodging intensities was analyzed, and the correlation between the ratio of canopy visual stem, leaf and panicle and lodging angle under different lodging intensity was analyzed, and parameters of sensitive agronomy that can effectively represent the lodging intensity was screened. A response model between rice canopy spectral indices and sensitive agronomic parameters was constructed by grey relational analysis to realize the spectrum diagnosis of rice lodging disaster, and field-measured samples were used to evaluate the diagnostic accuracy. The results showed that with the increase of lodging strength, the canopy spectra showed regular changes, red-band and near-infrared band response was more obvious, “Red edge” position is obviously “blue shift”, and “red edge” amplitude and “red edge” area increase, it shows that the red-band and near-infrared band on rice lodging stress intensity is more sensitive. The correlation of the canopy visual leaf-stalk ratio and lodging strength decreased with the increase of lodging strength, which was more than 0.715, indicating that the visible leaf stem ratio of canopy was better in characterizing the lodging strength. Through correlation analysis between visual leaf-stem ratio and hyperspectral reflectance, 698 and 1 132 nm in the red and near-infrared bands were respectively selected as the sensitive bands, and then the characteristic vegetation index was calculated. The spectral response model of rice visual leaf-stem ratio based on characteristic vegetation index was constructed by using grey correlation analysis, and the determining factor for the test sample was 0.635, and the precision of the classification of the disaster level with the visual leaf-stem ratio inversion result reached 82%. Therefore, the contribution proportion of stem, leaf and panicle in the canopy of rice in the field of spectral detectors was changed regularly after lodging. The difference of spectral reflectance and the ratio of apparent field in the Miho of stem, leaf and panicle is directly reflected in the spectral difference of lodging rice canopy. While visual leaf-stem ratio can effectively characterize the population structure change of rice under lodging stress, which has a good response relationship with the lodging intensity. The response law of visual leaf-stem ratio and rice canopy spectrum of different lodging intensity can effectively distinguish the lodging intensity, which will help provide a prior knowledge for remote sensing monitoring of rice lodging at the regional scale.