Attribute reduction is one of the core research problems in rough set theory， aiming to identify a more concise subset of attributes without compromising the classification capability of the original dataset. Among various reduction strategies， attribute reduction based on generalized decision preservation has been widely applied due to its strong interpretability and practical applicability. However， existing algorithms for generalized decision-preserving attribute reduction often suffer from significant computational inefficiency when dealing with large-scale datasets. How to efficiently achieve attribute reduction under the generalized decision-preserving framework has become a critical issue that demands urgent attention. To address this problem， this paper explores the stability characteristics of objects in the positive region during the generalized decision-preserving process. By employing the stripped quotient set method， a simplified and more representative equivalence class structure is constructed. On this basis， we propose an efficient heuristic attribute reduction algorithm under the generalized decision preservation framework. The proposed algorithm significantly improves computational efficiency while ensuring the correctness of the reduction results. Experimental results on eight benchmark datasets from University of California, Irvine (UCI) repository demonstrate that the proposed algorithm achieves higher reduction efficiency compared to existing methods.