Disclosed in the present invention is a quantitative spectroscopic data analysis and processing method based on deep learning. In the present invention, data does not require preprocessing, and effective information and background information can be learned from original spectroscopic data, improving the accuracy of quantitative spectroscopic analysis. In the present invention, high-dimensional features in spectroscopic data are extracted by means of three convolutional layers. In the second layer, a 1x1 convolution kernel is used, capable of dimensionality reduction and of decreasing the volume of calculations, and in the third convolutional layer, three differently sized convolution kernels are used, capable of learning, from the original spectroscopic data, differently sized features hidden in the spectroscopic data. In the present invention, data is not preprocessed, and original data may be processed directly. When the noise distributions of testing-set spectra and training-set spectra are different, the generalization ability of the present invention is higher.
QUANTITATIVE SPECTROSCOPIC DATA ANALYSIS AND PROCESSING METHOD BASED ON DEEP LEARNING