Aiming at the phenomenon of “foreign objects with the same spectrum” in the camouflaged target and the background target in certain specific environments, traditional visible light and multi-spectral remote sensing technologies have limitations in camouflage recognition. This paper, applies hyperspectral technology to the characteristic analysis and recognition of typical camouflage materials. The SVC HR1024 spectrometer was used to obtain the Visible-NIR Spectrum of the jungle camouflage net under different water immersion times. The spectral characteristics and sensitive bands of the jungle camouflage net under different water immersion conditions and typical vegetation in northern China were analyzed and revealed through spectral similarity measurement and envelope removal treatment. Based on the near-infrared band, the spectral ratio index (RCI) was constructed to identify the camouflaged targets in the green vegetation environment. Finally, the hyperspectral image in the simulation camouflage environment was obtained through a hyperspectral imaging experiment, and the recognition effect was verified using the hyperspectral image. The results showed that: (1)The basic morphology of the spectral curve of the jungle camouflage net with different water immersion times was similar, and its reflectivity decreased as a whole with increasing water immersion time. The 1 900 nm band is the most obvious band that the reflectance spectrum of jungle camouflage net responds to water content, and its spectral characteristics are similar to those of vegetation due to water immersion treatment, and the similarity is increased from 0.895 to 0.939. (2)The similarity between camouflage net and vegetation is high in the visible band, and the spectral fluctuation is similar, but the spectral characteristics of the jungle camouflage net and vegetation are different in near-infrared band. Through the analysis of the envelope removal method, it is concluded that the bands around 970, 1 190 and 1 440 nm are sensitive bands for identifying the jungle camouflage net. Moreover, based on the two obvious differences in reflectance slope between the jungle camouflage net and the vegetation in the band range of 900~1 900 nm, RCI1 (R1 190/R1 270) and RCI2 (R1 270/R1 440) were constructed. (3) The decision tree classification model based on the RCI index can quickly and effectively extract the camouflaged target from the green vegetation background. Experimental results show that using the RCI index to identify and extract the camouflaged target area, the results obtained are in good agreement with the original image in shape and size, and the recognition accuracy can reach 95%, indicating that the index has a good recognition effect on camouflaged targets.