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However, existing methods often inadequately exploit spatial\u2013spectral correlations and lack datasets tailored to clinically relevant scenarios. This work presents a systematic framework integrating a near\u2010infrared hyperspectral imaging system with a deep learning for accurate classification of common wound bacteria. The multi\u2010class and multi\u2010level dataset is first established using a 400\u20132500\u2009nm halogen\u2010illuminated system under controlled conditions, comprising 26\u2009056 image patches covering seven bacterial species at five concentration levels, totaling 31 classes. To fully harness spatial and spectral information, the Spatial\u2013Spectral Interactive Selection Network (SSIS\u2010Net) is proposed. Evaluated on the self\u2010constructed dataset, SSIS\u2010Net achieves an overall accuracy of 97.40%, an average accuracy of 97.37%, and a Kappa coefficient of 97.31%, consistently outperforming state\u2010of\u2010the\u2010art methods. 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