Due to the aircraft wing’s topological structure and lightweight design requirements, strain sensors installed on the wing are very limited. Traditional methods, relying on limited sensor data as a single information source, are insufficient for full-stress field monitoring, leading to a high prediction error. To address this issue, a novel wing stress field reconstruction method with limited measurement points is developed via multi-source heterogeneous information fusion. To be specific, two information fusion modules are designed to jointly overcome the challenges of limited measurement data and high non-linearity during full-stress field reconstruction. On one hand, the finite element mechanism-based information fusion module (FEMIFM) is proposed to derive and establish a mechanical model that relates the wing stress to positional parameter, in order to introduce physical information and reduce the non-linearity of the reconstruction mapping. On the other hand, the simulation stress expectation-based information fusion module (SSEIFM) leverages stress expectations derived from simulated stress fields under various operating conditions to incorporate statistical information, thereby enhancing the robustness and reasonableness of reconstruction results. Moreover, a soft-threshold loss function is proposed, which ignores zero-drift errors of strain sensors, improving the reconstruction accuracy of critical stress points. Finally, the developed method can be seamlessly integrated with popular neural networks (i.e., Transformer, convolutional neural networks, multilayer perceptron, etc.). Extensive experiments are conducted to validate the effectiveness of the developed method on an actual aircraft wing stress dataset.