To solve the problem of high-dimensional multi-modal optimization in practical engineering, a multi-strategy evolutionary quantum particle swarm optimization (QPSO) algorithm based on dynamic neighborhood is proposed.For the "premature" problem of particles in QPSO algorithm, this paper first defines a dynamic neighborhood selection mechanism to maintain the "activity" of the population and then combines the dynamic neighborhood mechanism to define the local attractor update equations of three different strategies to maintain the "diversity" of population evolution.In order to prevent the evolutionary direction of the algorithm from diverging, the local attractor update strategy converging to the global optimal solution is given greater weight.In the end, the comprehensive evaluation method of the wolves optimization algorithm is introduced to expand the optimal solution space.The experimental based on different types of high-dimensional multi-modal benchmark functions show that compared to the other four optimization algorithms, the proposed optimization algorithm has obvious advantages in convergence accuracy and stability, and this advantage becomes more prominent with the increase of testing dimensions, which shows a better performance in solving high-dimensional multi-modal optimization problems.The comprehensive evaluation method introduced in this paper has a high effective number of times in all test functions.The comprehensive evaluation of the effective means to find a more favorable evolution direction for the next evolution and further enhance the accuracy convergence.