<p>Steel surface defect classification constitutes a critical component of industrial quality control. Current computer vision-based classification approaches, however, exhibit substantial dependence on dataset characteristics, where inconsistent defect dimensions and low-contrast ambiguities significantly compromise detection accuracy. While emerging State Space Models (SSMs) such as VMamba and Vision Mamba offer efficient long-range modeling, they often lack the multi-granular feature extraction capabilities necessary for identifying such subtle industrial anomalies. To bridge this gap, we propose HiAM-Mamba, a hierarchical architecture that synergizes the linear complexity of SSMs with adaptive multi-scale processing. Distinct from existing Mamba variants, our approach integrates a Multi-Scale Fusion (MSF) module to aggregate features across varying granularities and a Gated CNN to capture fine-grained textural patterns. Furthermore, we introduce an Adaptive Feature Recalibration mechanism to dynamically suppress environmental noise and a hierarchical head for holistic decision fusion. Extensive experiments on the NEU-CLS and X-SDD benchmarks demonstrate that HiAM-Mamba achieves state-of-the-art accuracy (99.90% and 99.15%), significantly outperforming current CNN and Transformer-based methods while maintaining superior computational efficiency.</p>