To overcome the low accuracy and inefficiency of the conventional influence coefficient method for micromotor rotor balancing, this paper proposes a rotor unbalance identification approach based on a long short-term memory (LSTM) neural network. Synthetic vibration responses generated from the rotor-support dynamic model are used to pre-train the network, which is then fine-tuned with measured data, so that the knowledge learned from the idealized model is adapted to real measurement conditions. After min–max normalization, the data are fed into a multi-layer LSTM network that directly predicts the unbalance masses and phases on both correction planes from a single constant-speed vibration measurement. Simulation results show that the identification error remains below 5% for unbalance masses above 40 mg and within 4 mg for smaller masses. Experimental validation on three-slot and five-slot micromotor rotors demonstrates that the residual unbalance after correction is reduced by 10.8–17.4% depending on rotor type and correction plane, corresponding to an overall reduction of 15.4% in total residual unbalance mass. The proposed method eliminates repeated start–stop and trial-weight operations, significantly improving balancing efficiency. This work provides an effective approach for high-precision and rapid balancing of micromotor rotors.