An algorithm called the nonintrusive load monitoring (NILM) technique is used to identify legal electrical equipment by observing the trend over time or by performing an analysis in the frequency domain, comparing it with existing data or model [20; 21; 22]. Electromagnetic emissions were measured from several radio receivers to demonstrate the possibility of detecting and identifying these devices based on the use of a neural network in [23]. A solution for automatically detecting and classifying the operating status of electronic devices in a home from a single point of sensing based on conduction emissions is displayed in [24]. In [25], the author used signal strength to detect devices. At the printed circuit board (PCB) or circuit level, the radiation of the clock's differential signal on the PCB was preliminarily analyzed in [26]. The authors in [27] presented a method of classification of a PCB with several configurations, using a neural network to recognize its spectrum features. The authors in [28] proposed leveraging EM side-channels to recognize/authenticate components integrated onto a motherboard using the K-nearest neighbor algorithm. In [29; 30], an analytic model was developed for the unintended emissions of clocked digital devices, such as micro-controllers, which can be used as initiators to quickly detect explosive threats. Modulation frequency analysis was proposed as a tool to individuate broadband electromagnetic noise sources in large equipment in [31]. In [32], the authors proposed a jamming signal detection method based on truncated singular value decomposition (TSVD). In [33], the modulated unintended emissions from electronic devices were studied for the purposes of remote identification. The authors in [34] proposed a technique based on wavelet packets to perform feature extraction from the disturbance signals, as well as the classification of the extracted features in order to identify the possible causes of the disturbance. Similarly, [PERSON] et al. applied the wavelet transform method to emission data feature extraction and located the conducted emission problem in [35]. After years of research, a theory known as \"basic emission waveform theory\" (BEWT), characterizing emissions by the means of four basic waveforms, was proposed on the basis of summarizing tens of thousands of electromagnetic interference test data and the physical characteristics of common electronic equipment [36]. Furthermore, the characteristics of four basic waveforms were analyzed and classified via the adaptive boosting (Ada-boost) algorithm in [37], which demonstrated the potential of this theory in emitter identification and classification. This work was further deepened by the research of [38; 39; 40]. However, the above work was mostly based on conductive emissions, and identification and classification based on radiation emissions need to be studied further. In order to fulfill EMC requirements, several electromagnetic emission measurements are required to evaluate whether an electromagnetic emission exceeds the standard before the equipment is put into use. This means that these data accompany the entire life cycle of the equipment. In contrast with traditional functional testing, EMC testing records almost all functional and non-functional emissions, which can be viewed as containing all the radiation emission information of the equipment. In this paper, the EMC test scheme was followed to obtain the unintentional radiated emission spectral data. However, for convenience, only the part of the spectral data between 30 MHz and 200 MHz was intercepted for discussion in this paper. With the spectrum obtained in EMC testing, on one hand, a large amount of spectral data from different pieces of equipment in a single measurement need to be classified, and, on the other hand, when organizing the test again, it is necessary to identify whether the spectral data of the new test is that from specific known equipment.