Aiming at the complexity, switching frequency and control performance of the three-vector model predictive current control algorithm for doubly-fed induction generators, this paper proposes an improved threevector model predictive current control algorithm. The algorithm aims to reduce the computational complexity while fixing the switching frequency and improving the control performance. Firstly, the d-axis and q-axis deadbeat control principle of rotor current is used to fast vector selection is performed on the first and second optimal voltage vectors, so as to improve the efficiency of vector selection. Secondly, based on the d-axis and q-axis deadbeat control principle of rotor voltage, the action time of voltage vectors is allocated to reduce the computational complexity of vector action time. Finally, the action sequence of voltage vectors is optimized according to the principle of fixed switching frequency in each control period to fix the switching frequency. The simulation and experimental results show that compared with the three-vector model predictive current control algorithm, the algorithm shortens the running time, effectively reduces the rotor current, electromagnetic torque and output power ripple while fixed switching frequency and has good control performance.