The early and rapid detection of food-borne pathogens still challenges global food safety. Although routine tests such as selective agars served as the gold standard for decades, these methods are time consuming, often exceeding the best control time for foodborne bacterial outbreak. A hyperspectral microscopic imaging (HMI) technology coupled with an artificial intelligence algorithm was proposed to detect the common foodborne bacteria. The HMI technology has a natural trait in generating robust high-resolution spatial and spectral characterization at the cellular level. In this study, the Bi-directional long short-term memory (Bi-LSTM) was employed in the classification of Campylobacter jejuni (C. jejuni), Escherichia coli O157:H7 (E. coli), Salmonella Typhimurium (S. Typhimurium) based on morphological and spectral features extracted from single cell hypercubes. Compared with traditional linear discriminant analysis (LDA, 80.1%) and principal components analysis with support vector machine (PCA-SVM, 88.5%) classifiers, our proposed Bi-LSTM achieved the highest accuracy of 91.0% on the spectral dataset. Serious false-positive problems occurred in recognising E. coli and S. Typhimurium. However, with the involvement of morphological features, the discriminability of all classifiers was significantly improved. The proposed Bi-LSTM classifier achieved the highest accuracy of 98.1% based on the morphological-spectral feature dataset, while the LDA and PCA-SVM all achieved an accuracy of 95.3%. Our study demonstrated the applicability of HMI technology for foodborne bacterial cell characterization. Furthermore, with the advantage of Bi-LSTM in instantly processing the high-dimensional spatial-spectral features, the intelligent HMI shows great potential for rapid detection of foodborne pathogens.