Provided are a signal classification method and a device, the method includes: acquiring the terahertz time-domain signal of the biological tissue (101); processing the terahertz time-domain signal to obtain the wavelet entropy and wavelet energy features (102); constructing the feature vector based on the wavelet energy and the wavelet entropy (103); performing dimensionality reduction processing on the feature vector, and inputting the feature vector after dimensionality reduction processing into the preset machine learning classifier for recognition, so as to realize the recognition and classification of the terahertz time-domain signal based on the obtained recognition and classification result (104). Through the wavelet transform of the terahertz time-domain signal, the feature vector is constructed based on the wavelet energy and entropy, while considering energy information, the important information of complexity is introduced into the feature vector, which enriches the sample information carried by the feature vector; and through performing dimensionality reduction on the feature vector, the classification and recognition speed is improved.
SIGNAL CLASSIFICATION METHOD AND DEVICE