With the continuous improvement of port automation, remote control of container gantry cranes has become mainstream. However, real machine training poses significant safety risks, where novice drivers (Trainee) are prone to equipment collisions and violent container swings during operations. To address this issue, this paper designs and implements a digital twin system integrated with an operation risk assessment module, used for safety reproduction and risk quantification of gantry crane operations. The system adopts a time series prediction method based on attention mechanism, establishing a safety benchmark by modeling skilled drivers' (Trainer) operational data, and predicts and compares novice drivers' operational behaviors to quantify risk deviations. Experimental results demonstrate that the system can effectively distinguish operation risks across different driver experience levels, showing excellent application value in avoiding real machine training hazards, improving training efficiency, and achieving personalized risk assessment.