COVID-19, which has lasted for a year, has caused great damage to the global economy. In order to control COVID-19 effectively, rapid detection of COVID-19 (SARS-CoV-2) is an urgent problem. Spike protein is the detection point of Raman spectroscopy to detect SARS-CoV-2. The construction of spike protein Raman characteristic peaks plays an important role in the rapid detection of SARS-CoV-2 using Raman technology. In this paper, we used Deep Neural Networks to construct the amideⅠ and Ⅲ characteristic peak model of spike proteins based on simplified exciton model, and combined with the experimental structures of seven coronaviruses (HCoV-229E, HCoV-HKU1, HCoV-NL63, HCoV-OC43, MERS-CoV, SARS-CoV, SARS-CoV-2) spike proteins, analyzed the differences of amideⅠ andⅢ characteristic peaks of seven coronaviruses. The results showed that seven coronaviruses could be divided into four groups according to the amideⅠ and Ⅲ characteristic peaks of spike proteins: SARS-CoV-2, SARS-CoV, MERS-CoV form a group; HCoV-HKU1, HCoV-NL63 form a group; HCoV-229E and HCoV-OC43 form a group independently. The frequency of amideⅠ and Ⅲ in the same group is relatively close,and it is difficult to distinguish spike proteins by the frequency of amideⅠ and Ⅲ; the characteristic peaks of amideⅠ and Ⅲ in different groups are quite different, and spike proteins can be distinguished by Raman spectroscopy. The results provide a theoretical basis for the development of Raman spectroscopy for rapid detection of SARS-CoV-2.