To fully use the knowledge of problem-solving process and improve the computational resource utilization efficiency of dynamic multimodal optimization algorithms, an adaptive dynamic multimodal differential evolution algorithm based on knowledge guidance is proposed. Firstly, a self-organizing mapping(SOM) neural network is used to realize the self-clustering of population and form some stable niches. Secondly, through the comprehensive learning of the population global knowledge and the individual neighborhood knowledge, a knowledge-guided adaptive differential evolution(KADE) algorithm is designed to layer by layer guide the individuals to adaptively choose the mutation strategies that best meets their evolutionary demands. The proposed algorithm can improve the search efficiency of population and balance the diversity and convergence. Finally, when a change happens, an adaptive dynamic response strategy based on the historical experience learning is proposed to predict the positions of the elite individuals in the new environment to achieve a fast convergence. Experimental results show that the proposed SOM-KADE shows superior performance compared with the state-of-the-art algorithms.