Non-Intrusive load monitoring (NILM) is a technique to disaggregate the power consumption of the appliances from the total power reading of the household electricity meter. Even for the same type of appliances, their state types, the duration of each state and the power waveform of each state are different, which requires high generalization ability of the model based on feature engineering and clustering. Meanwhile, the disaggregate power of the regression model is difficult to quickly track the ground true power. To solve these problems, the regression problem is transformed into a multi-classification problem for each moment in the sequence, and a non-intrusive load monitoring model based on attention mechanism and residual networks is proposed in this paper. The proposed model is based on the seq2seq framework with encoder and decoder. Firstly, the high-dimensional sparse one-hot vector is mapped to the low-dimensional dense vector through the embedding matrix. In the encoder, BiGRU is used to extract the sequence information from the front and back directions, an attention mechanism is introduced to calculate the most important information at the current time in the sequence, and a residual connection is introduced to learn the difference between the input and output of the residual part. In the decoder, the regression layer is used to combine the BiGRU decoding results, and the maximum probability power category processed by a softmax function is taken as the result. The model performs well in the REFIT dataset, and the testing dataset and training dataset are completely independent, which indicates that the trained model can be directly applied to new households.