In order to solve the problems of slow convergence speed and easy to fall into local optimum, a Black-winged Kite Algorithm (EBKA) with multi-strategy improvement was proposed. Firstly, the tracking prey location update strategy is introduced to improve the global search ability of the algorithm and accelerate the convergence speed. Secondly, an adaptive t-helix strategy is proposed in the attack stage to prevent the algorithm from falling into local optimum. Finally, in the migration stage, when the leader of the black-winged kite loses its leadership role, the Levy tangent flight strategy is proposed to avoid the premature convergence of the algorithm. In order to verify the improvement effect of the algorithm, 8 test functions were selected for testing and compared with 5 swarm intelligence algorithms. Experimental results show that compared with other swarm intelligence algorithms, EBKA can quickly find the theoretical optimal value of 0 on the single-peak function, converge to the optimal value in about 30 times in the multimodal functionF_5, F_6 andF_8, and converge to the theoretical optimal value of 0 in the F_6 andF_7 It is proved that EBKA has good convergence performance, stability and global optimization ability.