With the development of deep learning technology, the non-intrusive load identification algorithm based on deep learning has become a research hotspot. In this paper, bilateral long-term and short-term memory network (Bi-LSTM) is used for load identification for the first time. A non-intrusive load identification algorithm based on Bi-LSTM is proposed. By locating the moment of load event, the steady state information (active power, reactive power and 15th odd-even current harmonics) of the load operation state are combined as the input of Bi-LSTM which is trained. And precision, recall, accuracy and F1 values are used as evaluation indicators. The results show that the method can identify low-power and multi-state appliances, and Bi-LSTM has stronger recognition ability than long-term and short-term memory network (LSTM) and recurrent neural network (RNN). For the case where there are multiple electrical appliances running in a time period, a method based on the starting and ending feature matching of the load running state is proposed, and the feasibility of the algorithm is proved through experiments.