Aiming at the problems of low recognition rate and hyper-parameter setting of deep learning model in electric load recognition, a non-intrusive electric load recognition model (PSO-CNN) combining particle swarm optimization algorithm (PSO) and convolutional neural network (CNN) was proposed. First, the pixelated image of VI trajectory of each appliance is used as the CNN input feature. Secondly, the influence of CNN hyper-parameter on model performance was analyzed, and PSO algorithm is used to find the optimal solution to improve model recognition effect. Finally, the PLAID and WHITED public datasets were used to compare and verify the PSO-CNN model. The experimental results show that the recognition accuracy and average F-measures of this model are better than other models. The model effectively reduces the confusion between devices and has good recognition and generalization ability.