Multi-trait automated essay scoring extends holistic scoring by evaluating essays across multiple writing dimensions. In this framework, inter-trait consistency signals provide an effective way to improve prediction across traits. However, learning shared scoring signals may obscure trait-specific features, while enforcing consistency across traits can introduce negative transfer, hindering the model’s ability to distinguish individual traits. To solve these issues, we propose DPRES, a dual-perspective decoupled representation learning framework that disentangles consistency and specificity at both the feature and optimization levels. Specifically, we first propose a residual subspace decomposition strategy that enforces orthogonality between shared scoring features and trait-specific features, thereby disentangling shared scoring signals from trait-specific scoring signals in the latent representation space and enhancing the model’s ability to capture trait-specific scoring features. Based on the subspace decomposition strategy, we further design a consistency–specificity decoupled learning strategy to simultaneously preserve inter-trait correlations and enhance trait-level discrimination. Experiments on public datasets demonstrate that the proposed method consistently outperforms strong baselines.