The seed cotton cleaning machine, as the core of cotton processing, faces operational challenges that limit overall efficiency. These include the inability to monitor equipment status in real time, delayed and passive fault alerts, and loosely defined operation and maintenance strategies during the impurity removal process. Such limitations have constrained further improvements in cotton processing quality, production efficiency, and overall enterprise profitability. To address these issues, digital twin technology was applied to create a virtual replica of the physical seed cotton cleaning machine. Based on a detailed analysis of the machine’s operational principles and mechanical structure, a high-fidelity digital twin model was constructed. This model established a dynamic, bidirectional mapping mechanism between the physical machine and its virtual counterpart, enabling seamless data exchange and state synchronization. Using the Unity platform, a comprehensive digital twin monitoring system was developed for the seed cotton cleaning machine. This system integrated real-time data acquisition, simulation, and analysis capabilities. It allowed for real-time monitoring of the machine’s operational status, facilitated proactive fault warnings through predictive analytics, and supported dynamic optimization of process decisions based on simulated scenarios. Performance evaluations of the system demonstrated strong stability and reliability with key metrics, including a data packet loss rate of 0, a CPU usage rate of approximately 5%, an average GPU memory occupancy of around 4%, and an average motion simulation frame time of 20.416ms. The system was verified to possess excellent stability, reliability and robustness.