Non-intrusive load monitoring is a technology that identifies operating electrical appliances and their energy consumption by analyzing voltage and current variations on the main power bus. With the continuous increase in the types and quantities of electrical loads, extracting unique load features and establishing efficient non-intrusive load monitoring classification models have become particularly important. This paper proposes an image feature enhancement method centered on multi-voltage-current trajectory fusion and background feature mapping, and applies it to the ResNet18 network via a transfer learning strategy. By means of multi-V-I trajectory fusion and image background mapping, the accuracy of load identification is improved, thus achieving efficient classification of non-intrusive loads. Different from traditional methods, this paper for the first time proposes a differential fusion strategy of full-wave and filtered trajectories, which enhances the uniqueness of load features. Additionally, by mapping multiple steady-state features in the image background, the representational capability of the images is further improved. Experimental results demonstrate that the load identification accuracies of this method on the PLAID2014, PLAID2017 and PLAID2018 datasets are increased to 98.67%, 97.53% and 98.64% respectively, exhibiting significant advantages over existing models.