Tandem mass spectrometry (MS/MS)-based proteomics has become one of the most important tools in bioscience, and researchers now pay much attention to the prediction of MS/MS spectra for protein identification and quantification. With the accumulation of massive high-quality spectrum data and the development of computing technology, quite a few new methods were emerged to solve this problem. These methods can be divided into two catagories：mobile proton model-based methods, such as MassAnalyzer and MS-Simulator; and machine learning-based methods, including traditional machine learning and deep learning, such as PeptideART, MS2PIP, MS2PBPI and pDeep. In this paper, we investigated a wide variety of corresponding methods, and briefly pointed out the deficiencies of existing software tools, and suggested the future work.