The electronic density of states (DOS) is key to understanding material behavior, but is computationally expensive to calculate via density functional theory (DFT), especially for complex systems. We propose a self-consistent encoder-decoder neural network that learns bidirectional mappings between local atomic environments and projected DOS (pDOS), trained via dual-loss optimization to enforce physical consistency. Applied to equiatomic HfNbTiZr high-entropy alloys, the model achieves root-mean-square errors of 2.55 meV for the $d$-band center and 0.02 states/(eV atom) for Fermi-level DOS, surpassing direct structure-level predictions. The approach generalizes well to ${\mathrm{SrTiO}}_3$ and vacancy-ordered perovskites, demonstrating broad applicability. By enabling accurate DOS prediction, multi-objective optimization, and potential inverse design, this framework offers a robust pathway for data-driven electronic structure modeling in complex materials.

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• Density of states
• Electronic structure
• First-principles calculations
• Alloys
• Oxides
• Data analysis
• Machine learning