Addressing the growing demand for electric vehicle(EV)charging and the contradiction between the uneven distri-bution of public charging piles and the high vacancy rate of private charging piles,the author proposes an intelligent recommenda-tion model for electric vehicle charging piles based on the MultiLayer Perceptron(MLP).The model integrates user requirements,characteristics of charging facilities,and historical user behavior data,employing feature extraction techniques to construct a fea-ture set,and utilizes the MLP algorithm for training and optimization to achieve personalized charging pile recommendations.Com-pared with the Random Forest algorithm,this model demonstrates superior performance in providing personalized charging ser-vices for users,effectively enhancing the utilization rate of charging resources and alleviating the contradiction between the supply and demand of charging piles.
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