The non-intrusive load decomposition is to decompose the power signal of a single load device according to the known total power signal.However,deep learning based models are perplexed by problems such as insufficient load feature extraction,low decomposition accuracy,large decomposition error for infrequently used load equipment.Here,we propose an Attention Recurrent Neural Network (ARNN) model,which combines regression network and classification network to realize the non-invasive load decomposition.The model extracts the features of sequence signals through RNN network,and uses the attention mechanism to locate the position of important information in the input sequence,so as to improve the representation ability of network.Experiments on public datasets of Wiki-Energy and UK-DALE show that the proposed deep neural network is superior to the most advanced neural network under all experimental conditions.Furthermore,the attention mechanism and auxiliary classification network can correctly detect the on or off of devices,and locate the high-power signal,which improves the accuracy of load decomposition.