As Industrial 4.0 unfolds and digital twin technology rapidly advances, modeling techniques that can abstract real-world industrial objects into accurate and robust models, referred to modeling for industrial objects (MIO) tasks, have become increasingly crucial. However, existing works still face two major limitations. First, each of these works primarily focuses on modeling a specific industrial object. When the industrial objects change, the proposed methods often struggle to adapt. Second, they fail to fully consider latent relationships within industrial data, limiting the model’s ability to leverage the data and resulting in suboptimal performance. To address these issues, we propose a novel modeling paradigm tailored for MIO tasks, named UltraModel. Specifically, a twin model graph module is designed to construct a customized graph based on the mechanisms of industrial objects and employ graph convolution to generate high-dimensional representations. Then, a multi-scale feature abstraction module and a spatial attention-based feature fusion module are proposed to complement each other in performing multi-scale feature abstraction and fusion on high-dimensional representations. Finally, the outputs are obtained by processing the fused representations through a feedforward network. Experiments on two different industrial objects demonstrate our UltraModel outperforms existing methods, offering a novel perspective for addressing industrial modeling challenges.