For the multi-information fusion feature and multi-system modeling complexity of dynamic driving behavior and random charging behavior for electric vehicles(EVs), a charging navigation strategy of EVs based on multi-information interaction and deep reinforcement learning (DRL) is proposed in this paper. In this strategy, the EV actual operation data collected by ‘optimization energy storage cloud platform of electric vehicle clusters’ is firstly modeled and mined. Through data preprocessing and data visual display, the EV driving and charging information as well as urban charging station information are obtained. Then, the EV charging scheduling process conforming to Markov decision process (MDP) is analyzed, and the DRL is introduced to establish a charging navigation model. The real-time information of ‘vehicles-stations-networks’ is regarded as the state space of deep Q network(DQN), and the allocation of charging station is taken as the execution action of agents. Based on the evaluation of travel cost and time decision-making objectives in different periods of charging process, the reward functions of enroute and arrival a station is determined. The optimal action value function corresponding to the highest reward is implemented to recommend the optimal charging station and plan for the driving path for the owner. Finally, a multi-scene simulation example is designed to verify the feasibility and effectiveness of the strategy proposed in this paper.