Graph Neural Networks (GNNs), widely applied in social networks and knowledge graphs, face significant computational bottlenecks. Redundancy elimination has shown promise in optimizing GNNs, but existing methods often sacrifice effectiveness for lower algorithmic time cost, limiting their acceleration capabilities. To address this, we propose ORE (Offline Redundancy Elimination), a system with two components: A multi-level iterative strategy to expand the redundant data pool; A redundancy elimination algorithm formulated as a maximum weight clique problem, solved using an advanced solver to maximize elimination performance. Experiments on public datasets show that ORE achieves up to 10.4× end-to-end speedup for GCN—4.6× higher than previous SOTA methods—and improves redundancy elimination by 3.7×.