Aiming at the problems of negative knowledge transfer and low efficiency of the traditional multi-tasking optimization algorithm(MTEA), a multi-tasking differential evolution algorithm based on super-particle guided adaptive knowledge transfer(SAKT_MFDE) is proposed. Firstly, the algorithm adaptively adjusts the mating probability between tasks by the similarity degree between tasks to increase the forward migration between tasks. Secondly, the super-particle is used to guide the search direction of the algorithm, which further improves the overall optimization efficiency of the algorithm. The optimization performance of the improved algorithm is evaluated by the simulation of the multi-task benchmark function. The experimental results show that the proposed algorithm can effectively avoid the negative migration between tasks and improve the optimization performance of the task group with low similarity.