Understanding the local electronic structure is essential for comprehending the structure and properties of materials. One of the approaches to access the local electronic structure is the density functional theory (DFT) calculations, where we can analyze the relationship between the atomic and electronic structures in detail. Furthermore, by constructing machine learning models on DFT data, we can rapidly predict and analyze the electronic structure from the corresponding atomic structures . For instance, the prediction of the electronic structure of silicon from local structure has been reported , but when one wants to experimentally analyze the electronic structure, it is difficult to fully determine the atomic structure, making predictions based on the atomic structure challenging. Moreover, it is desirable to directly extract information about the electronic structure from the measurement data without going through the process of structure determination. Among various characterization techniques promising for this purpose, core-electron spectroscopy, specifically electron energy loss near edge structure (ELNES) and X-ray absorption near-edge structure (XANES), stands out as a powerful method . ELNES and XANES capture a transitions of core electrons to conduction bands, thereby providing the insights into the electronic states of specific atoms. This enables the extraction of elemental and chemical information of the atom through the spectrum analysis, such as fingerprint verification and first principles simulations based on DFT . Previous studies have utilized ELNES for atomic-level elemental mapping, oxidization-state mapping, and chemical-bonding mapping, establishing it as a powerful analytical tool for characterizing local states . Moreover, XANES provides exceptional detection sensitivity and temporal resolution, enabling the investigation of chemical reactions in battery materials and beyond .