Performance evaluation is crucial in neural architecture search (NAS), but full training is costly and slow. Performance predictors offer an efficient way to quickly evaluate architectures, significantly speeding up the process. However, existing predictors often trade accuracy for speed or depend on complex encoders and costly pretraining, making it difficult to balance accuracy and efficiency with limited labeled data. In this paper, we propose a Bidirectional Multi-hop predictor with Structure-aware Ranking Loss (BMSR), which is designed for speedy and accurate performance prediction. During feature extraction, BMSR applies a bidirectional multi-hop graph convolution network with hop-aware attention to capture long-range and directional dependencies from architectures. Once the architecture embeddings are obtained, a progressively shrinking MLP is employed to compress them layer by layer, enhancing nonlinear modeling and improving representation quality. In the optimization stage, BMSR adopts a structure-aware ranking loss that leverages topological and operational similarity to encourage stable rankings among architectures. Experiments across multiple NAS benchmarks demonstrate that BMSR achieves competitive performance in both efficiency and accuracy. On NAS-Bench-201, BMSR identifies the optimal architecture using only 100 labeled samples and 8.45 s–just 0.3% of the computation time required by prior SOTA methods. Code is available at https://github.com/Thorn-222/BMSR .