Abstract: Proposed is a theory-aided few-shot learning algorithm applicable to any material elastoplastic relationships utilized in civil engineering. The new algorithm can effectively alleviate the paucity of data in real applications when applying large-scale deep learning models. The framework of the classical elastoplasticity theory is briefly recapped. On this basis, the paper elaborates on how to incorporate the elastoplastic equations into the ordinary deep learning models, wherein the entire processes require neither the concrete form of the underlying models nor its complicated numerical implementation, thus maintaining the simple, direct, and efficient advantages of data-driven techniques. To address the divergence issue caused by the complicated optimization target, a novel training strategy named overfitting-correction method is invented, which is able to stabilize the training and accelerate the convergence. A numerical experiment is performed upon a sophisticated elastoplastic constitutive model of structural steel. The results demonstrate that the theory-aided few-shot learning algorithm succeeds in realizing excellent generalization performance of a large-scale deep learning model on small training datasets. Quantitatively, the new algorithm overwhelms the pure data-driven model by 38.9%. The philosophy of theory-aided driven mode proposed can extend to the structural level in the future, facilitating the introduction of more advanced intelligent technologies into civil engineering.