Traditionally, to obtain information on the electronic states including both the valence and conduction bands at the ground state from ELNES/XANES, it has been necessary to compare the experimental spectra with theoretically simulated spectra or reference spectra, followed by further analysis through theoretical calculations. On the other hand, in recent years, the effectiveness of employing machine learning (ML) techniques in materials characterization has been demonstrated . ML has also increasingly been utilized in extracting information from ELNES/XANES spectra, with methods reported for directly predicting atomic structure and material properties from ELNES/XANES . Furthermore, it has recently been reported that it is possible to predict the electronic structure of organic molecules in their ground state from C-K edge ELNES/XANES .