In view of the characteristics of steel defects and the current classification methods' dependence on complex models and non-heuristic attention mechanisms, this paper proposes a multiattention-based classification method (MACM) for steel surface defect classification based on the CBAM (Convolutional Block Attention Module) hybrid attention architecture. Firstly, heuristic channel attention is achieved through gray correlation analysis to enhance the interpretability of the algorithm and overcome the impact of interlayer information loss on channel attention determination. Secondly, compact bilinear pooling is used to achieve spatial feature fusion, enhancing the capture of nonlinear complex interaction information between abstract features. Finally, the combination of channel and spatial attention is achieved based on the CBAM architecture. The experiments show that the MACM method not only performs well but is also lighter than existing state-of-the-art methods, validating the effectiveness of the MACM algorithm in improving classification accuracy and reducing model complexity.