The fingerprint characteristics of the terahertz absorption spectrum of materials have been widely used in material identification， but the strong absorption of terahertz waves by water vapor in the actual atmospheric environment will cause the spectrum to oscillate severely； there will be increasing false， weak， and aliased peaks. These phenomena have seriously affected the accuracy of peak-finding comparison and the ability of substance identification. In spite of this， on the basis of extracting the terahertz absorption spectrum of explosives at relative humidity of 2%， 15%， 35%， 45%， and 60%， the continuous wavelet transform is expanded in the frequency domain to obtain a unique characteristic. Then， the network training is carried out on the frequency domain scale maps of explosives obtained under the above 5 different humidity conditions based on the deep learning method with the ResNet-50 network model as the basic network structure； the classification accuracy of the test can be up to 96.6%. To verify the effectiveness of the technology under untrained humidity samples， the time-domain signals of explosives at relative humidity of 50%， 55%， and 67% were fed into the identification system； the classification accuracy could reach 96.2%. Experiments show that a new terahertz material identification method， based on wavelet transform and ResNet-50 network classification training， greatly improves the accuracy of material identification in high humidity environment compared with the traditional peak-finding method. In addition， it avoids a series of complex preprocessing operations such as noise reduction and smoothing， and considerably expands the engineering adaptability of terahertz spectral detection technology. It provides help for accurate detection and identification of mines and other explosives in high humidity and extremely complex special operations environments such as mountains， forests， and depressions.