Abstract: Most of the research on near infrared spectrum processing focuses on directly extracting convolutional features from the original one-dimensional spectral signals, in order to comprehensively explore the information in the spectral data and improve the modeling effect of classification models, a method for establishment of qualitative analysis model of Gentiana macrophylla by continuous wavelet transform and convolution attention module was proposed. The continuous wavelet transform was used to convert one-dimensional signals into two-dimensional image representations, and the obtained wavelet time-frequency maps were used as spectral features. A convolutional neural network qualitative analysis model Att-GoogleNet for the near infrared spectra of Gentiana macrophylla with attention mechanism was established, and the dataset was expanded by flipping, contrast enhancement, and adding Gaussian noise to achieve data augmentation and improve the generalization ability of the model. As shown by the results, this method was analyzed the origin of 207 samples of Gentiana macrophylla, and the classification accuracy of the Att-GouogleNet model was 99.6%, the accuracy, precision, recall, specificity and F1-Score were better than those of traditional machine learning models.