To improve the research efficiency of cylindrical flow-induced vibration and based on computational fluid dynamics numerical simulation and dynamic mesh UDF technology, combined with the analysis method of deep neural network (DNN), a prediction model for the lift coefficient of flow-induced vibration of a cylinder was constructed, and the regularity of the lift coefficient of the cylinder was explored. The results show that regardless of the mass ratio, the lift coefficient exhibits a trend of increasing first, then decreasing, and finally stabilizing with the increase in flow velocity. The range with large values is concentrated under small mass ratios and lower velocities, with the maximum value corresponding to a flow velocity of approximately 0.4 m/s. In summary, the DNN lift coefficient prediction model is simple and efficient, achieving fast and accurate data prediction, providing a new approach for studying the force characteristics of flow-induced vibration (FIV).