Abstract: Black tea is the second largest type of tea in China. Four stages are involved during black tea processing, such as the withering, rolling, fermentation, and drying. Among them, the fermentation can be closely related to the color and taste of black tea. Furthermore, the quality of black tea after fermentation can dominate its market value. In addition, the black tea with moderate fermentation is less prone to spoilage during storage. Mild fermentation or excessive fermentation can decrease in the preservation of tea, making them susceptible to external influencing factors on the unique quality of black tea. Therefore, it is often required for the precise control over the fermentation quality of black tea. At present, the fermentation quality of black tea can rely mainly on the experience of tea makers in "observing color, smelling aroma, and touching texture", which is highly arbitrary and subjective task. The quality of black tea has been limited to the standardization in production. In this study, an improved deep learning model was proposed to accurately evaluate the fermentation quality of black tea using image features and machine vision technology. The deployment of the model was considered under actual production environments. Firstly, the experimental comparisons were conducted on the seven models of convolutional neural network under the same conditions. Among them, the Ghostnet model shared the best discriminative performance, in order to select as the teacher model. Mobilenetv3_small was used as the student model after comparison, considering both the discriminative performance and complexity of the model. Secondly, a series of experiments were conducted to compare the discriminative performance, taking the AdamW, SGD, and RMSProp as the research objects. After that, the student and teacher model were replaced with RMSProp optimizer. The discriminative performance of the model was balanced between its complexity or speed. Afterwards, the loss functions of the student model Mobilenetv3_small and the teacher model Ghostnet were simultaneously changed into the CE Loss. Their discriminative performance was further improved for the less complexity of the models. There was the range of knowledge distillation loss ratio between 0.1 and 2.0. Knowledge distillation experiments were performed on the student model under SoftTarget using the teacher model. The results showed that the discrimination performance was significantly improved to maintain the complexity and speed, when the knowledge distillation loss ratio was 0.4, 1.7, and 1.9. In contrast, the discriminative performance of the model was improved the most, when knowledge distillation loss ratio was 1.9. The improved model was achieved in the Accuracy, Precision, Recall, and F1 of 96.93%, 95.15%, 95.79%, and 95.46%, respectively. These scores increased by 2.01, 2.67, 3.72 and 3.19 percentage points, respectively, compared with the original model. The fermentation quality of black tea can be precisely controlled to fully meet the fermentation quality of the black tea. The finding can also provide the strong technical support for the digital and intelligent processing of black tea.