Originating in China, mulberry is one of the fruits of the homology of medicine and food and has a long history. However, the industrialization of mulberry fruit has been limited by its characteristics of short maturity period and the tendency for thin skin to decay. Total soluble solid (TSS) is an important component of determining the mulberry flavor and qualityandis one of the most basic quality characteristics for its postharvest-commercialization. This study aims to optimize a prediction model for monitoring the TSS content in postharvest mulberry fruits using near-infrared hyperspectral imaging and deep learning methods and to evaluate the impact of common postharvest storage temperature on the quantitative models, thus providing support for rapid quality assessment of mulberry fruits. Mulberry fruits with consistent commercial maturity were selected for storage at room temperature (25 ℃) and low temperature (4 ℃). Samples from different storage stages were selected for spectral data collection and TSS content determination until mulberry fruits became unfit for consumption. Based on the spatial information provided by the corrected hyperspectral images, regions of interest were extracted to obtain representative spectra without background accurately. Then, standard normal variate (SNV), multiplicative scatter correction (MSC), and Savizkg-Golag (SG) smoothing were used for spectra preprocessing to improve the spectral signal-to-noise ratio. Prediction models for TSS content measurement in postharvest mulberry fruits were established using deep learning. For mulberry samples stored at room temperature and low temperature, the optimal CNN models obtained the residual prediction deviation values of 5.828 and 5.429, with the root mean square error of prediction (RMSEP) values of 1.082 and 1.099°Brix, respectively, indicating that the prediction performance of the CNN model was degraded due to the low-temperature storage. The classical machine learning methods of partial least squares (PLS) and least square support vector machine (LS-SVM) were used to establish models for TSS prediction further to verify the effectiveness of the constructed CNN models. Results showed that the nonlinear LS-SVM model was more suitable for predicting TSS content in mulberry fruits than the linear PLS model. For mulberry fruits stored at two different temperatures, the optimal LS-SVM models achieved RPD values of 4.221 and 4.423 for TSS prediction, respectively, indicating that the CNN performed better than the classical machine learning methods. In conclusion, hyperspectral imaging combined with deep learning CNN has excellent potential in predicting TSS content inpostharvest mulberry fruits, which provides technical support for rapid assessment of mulberry quality.