Traditional wood classification methods face significant challenges, including low efficiency, heavy reliance on manual expertise and prevalent material fraud in furniture markets. Additionally, there exists a research gap in the application of X-ray single-projection imaging for wood identification. To address these issues, a set of special imaging device for wood material visualization was independently developed and an intelligent wood authentication system that integrates X-ray single-projection imaging with a dual-attention mechanism was proposed. A systematic X-ray single-projection dataset comprising 10 typical commercial wood species was constructed, including three Pterocarpus species, four Dalbergia species, and three other woods. A key innovation of this study is the proposed EDMA-Net model. This architecture integrates a hierarchical GAM-ECA dual-attention mechanism that enables synergistic optimization of both shallow global texture features and deep channel characteristics. Experimental results indicate that the model achieves an overall classification accuracy of 98.04%. Ablation studies show that, compared to the baseline model, EDMA-Net boosts the classification accuracy by 2.38% while compressing the parameters to only 16.51 M. The average single-image inference time is only 7.00 ms, reaching an inference speed of 142.95 fps, thereby verifying the module′s effectiveness. Furthermore, actual furniture panels tests were conducted and the accuracy for all samples exceeded 95%. By leveraging X-ray single-projection imaging technology, this system provides a non-destructive, rapid and practical technical solution for material authentication, quality monitoring, and wood processing management in the furniture market.