Aiming at the limitation of using single equipment features for load identification, a non-intrusive load identification algorithm based on feature fusion and deep learning is proposed. Firstly, V-I trajectory image features and power numerical features are extracted by analyzing the high-frequency sampling data of the equipment. Then the fusion of V-I trajectory image features and power numerical features is realized by using the advanced feature extraction ability of artificial neural network (ANN). Finally, the back propagation (BP) neural network is trained to identify equipment by using fusion feature as the new feature of the equipment. The PLAID data set is used to verify the identification performance of the algorithm, and the performances of different classification algorithms are compared for feature fusion and load identification ability. The results show that the proposed algorithm makes use of the complementarity of different features, overcomes the disadvantage that V-I trajectory features cannot reflect the power of the equipment, and improves the load identification ability of V-I trajectory features. In embedded devices, the computing speed of the proposed algorithm can reach the millisecond level.