To address the limitations of the existing deep learning methods in handling scenarios where both geometric boundaries and initial conditions vary in physical simulation problems， a technical approach was proposed to decouple the representation of geometric boundary constraints from the physical system simulation， and a two-step technical route of geometric feature representation learning and physical system simulation was designed. After constructing an independent geometric feature extraction module which was unaffected by external physical conditions， the extracted geometric features were fused with physical features， and finally a neural network-based physical system simulation model was designed. In stress field prediction experiments， the proposed method achieves a prediction time of only 2.63 ms， which is much lower than 0.6 s of Finite Element Method （FEM）， and has a Mean Absolute Error （MAE） only 0.389 times of that of MeshNet. Experimental results demonstrate that the proposed method maintains high simulation accuracy while effectively adapting to different geometric boundaries and initial conditions.