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Potatoes are highly susceptible to internal defects such as black heart disease during storage, which seriously affects market value and food safety. To explore the problem of deep learning in mining the deep features of potato black heart disease spectral data, the near-infrared spectral data were two-dimensionalized, based on residual neural network (ResNet), convolutional block attention module (CBAM) was introduced to enhance the features, and a threshold processing module was added to remove the noise. The features were enhanced by introducing the CBAM, and the noise was removed by adding the thresholding module, which realized the rapid and nondestructive detection of black heart disease in potato. To explore the spectral two-dimensionalization method applicable to the detection of potato black heart disease, four methods, namely, Gramian angular field (GAF), Markov transition field (MTF), recurrence plot (RP) and wavelength-order conversion, were compared and analyzed. It was found that the three methods GAF, MTF and RP were better compared with wavelength-order transformation, and the best modeling effect was achieved after MTF processing, and the accuracy of the training set reached 99.60%. By comparing the performance differences of different models, it was found that the test set accuracy of the improved ResNet model was 97.65%, which was better than that of partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), MobileNet and ResNet by 5.89, 7.07, 3.53 and 2.36 percentage points, respectively, and the traditional chemometrics methods PLS-DA and SVM were not as effective as neural network models such as MobileNet and ResNet in modeling.