It is important to achieve an efcient home energy management system (HEMS) because of its role in promoting energy saving and emission reduction for end-users. Two critical issues in an efcient HEMS are identifcation of user behavior and energy management strategy. However, current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with diferent applications. This can lead to an insufcient description of behavior and suboptimal management strategy. To address these gaps, this paper proposes nonintrusive load monitoring (NILM) assisted graph reinforcement learning (GRL) for intelligent HEMS decision making. First, a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classifcation model is used to monitor the loads. Thus, efcient identifcation of user behavior and description of state transition can be achieved. Second, based on the online updating of the behavior correlation graph, a GRL model is proposed to extract information contained in the graph. Thus, reliable strategy under uncertainty of environment and behavior is available. Finally, the experimental results on several datasets verify the efectiveness of the proposed mode.