The control and optimization of the fermentation process are the key technologies in batch production of fuel ethanol, it has been attracting continuous attention in the control and optimization of a biological fermentation plant. Although many detection methods are proposed in the literature, the increasing demand for green renewable energy and the increasing reliability of biological fermentation plant also brings a new challenge to the control and optimization of the fermentation process. On the one hand, fuel ethanol has developed from laboratory scale to industrial scale production, but the traditional detection methods are also useful. On the other hand, the plant becomes extremely complex and intelligent which made the control optimization process more difficult. As a response, an automatic identification method for key time nodes of batch fermentation of fuel ethanol based on stacked sparse autoencoder (SSAE) and fuzzy C-means clustering (FCM) is proposed. Firstly, SSAE is employed to extract the high-level features of the original data (boolean data, process data, and energy consumption data) layer by layer from low to high levels. Then, the high-level features that reflect the essential attributes of the data are taken as the input data of FCM clustering operation, and an automatic identification model of batch time nodes in fuel ethanol fermentation based on FCM is established. Finally, the batch process data of fuel ethanol fermentation from SDIC Bioenergy Company is used as training samples to validate the SSAE-FCM. And the two indicators of recognition accuracy (RV) and recognition speed (RN) are employed to characterize the effect of the detection model. In contrast, the control of the fuel ethanol fermentation process based on kinetic models and multiparameter correlation analysis method are introduced to carry on optimization results under the same condition. By comparison, the results show that the method proposed in this work has better identification performance, which satisfies the requirements of batch process control of ethanol fermentation.