Spectrum classification method based on multi-branch attention network

The invention discloses a spectrum classification method based on a multi-branch attention network, and the method comprises the steps: employing a deep neural network composed of a feature extraction part and a classification part, and enabling the feature extraction part to comprise four layers of basic convolution blocks used for extracting the high-level semantic information of a spectrum, and four layers of multi-branch attention used for enhancing the output features of the basic convolution blocks; according to the feature extraction part, multi-branch attention is used for further learning feature data output by a basic convolution block, and important information of the feature data is enhanced. An original feature branch, a mixed space attention branch and a deep convolution branch included in the multi-branch attention respectively complete positioning and learning of global and local information of Raman feature peaks of feature data output by a basic convolution block, and feature fusion processi