Rapid reconstruction of 2D plasma profiles from line-integral measurements is important in nuclear fusion. This paper introduces a physics-informed model architecture called Onion, that can enhance the performance of models and be adapted to various backbone networks. The model under Onion incorporates physical information (PI) by a multiplication process and applies the physics-informed loss function (PILF) according to the principle of line integration. Prediction results demonstrate that the additional input of PI improves the deep learning model’s ability, leading to a reduction in the average relative error between the reconstruction profiles and the target profiles by approximately on synthetic datasets and about on experimental datasets. Furthermore, the implementation of the Softplus activation function in the final two fully connected layers improves model performance. This enhancement results in a reduction in the by approximately on synthetic datasets and about on experimental datasets. The incorporation of the PILF has been shown to correct the model’s predictions, bringing the back-projections closer to the actual inputs and reducing the errors associated with inversion algorithms. Besides, we have developed a synthetic data model to generate customized line-integral diagnostic datasets and have also collected soft x-ray diagnostic datasets from EAST and HL-2A. This study achieves reductions in reconstruction errors, and accelerates the development of surrogate models in fusion research.