Optoelectrowetting technology generates virtual electrodes to manipulate droplets by projecting optical patterns onto the photoconductive layer. This method avoids the complex design of the physical circuitry of dielectricwetting chips, compensating for the inability to reconstruct the electrode. However, the current technology relies on operators to manually position the droplets, draw optical patterns, and preset the droplet movement paths. It lacks real-time feedback on droplet information and the ability for independent droplet control, which can lead to droplet miscontrol and contamination. This paper presents a combination of optoelectrowetting with deep learning algorithms, integrating software and a photoelectric detection platform, and develops an optoelectrowetting intelligent control system. First, a target detection algorithm identifies droplet characteristics in real-time and automatically generate virtual electrodes to control movement. Simultaneously, a tracking algorithm outputs trajectories and ID information for efficient droplet arrays tracking. The results show that the system can automatically control the movement and fusion of multiple droplets in parallel and realize the automatic arrangement and storage of disordered droplet arrays without any additional electrodes and sensing devices. Additionally, through the automated control of the system, the cell suspension can be precisely cultured in the specified medium according to experimental requirements, and the growth trend is consistent with that observed in the well plate, significantly enhancing the experiment's flexibility and accuracy. In this paper, we propose an intelligent method applicable to the automated manipulation of discrete droplets. This method would play a crucial role in advancing the applications of digital microfluidic technology in biomedicine and other fields.