Non-intrusive load monitoring, as an essential means for fine-grained management of household electricity consumption, plays a significant role in promoting energy conservation and emission reduction for achieving the dual-carbon goal. However, it is challenging to achieve high-precision load identification using a single voltage-current trajectory image. Therefore, a non-intrusive load identification method based on the fusion of Gramian angular difference field (GADF) image coding is proposed. First, the high-frequency steady-state data collected by the device are preprocessed to obtain a complete base-wave period current and voltage signal. Then, the one-dimensional voltage and current signals are encoded separately using the GADF to generate the corresponding two-dimensional feature images, and load identification is performed via superimposed fusion input to a neural network based on a convolutional block attention module. The public datasets PLAID and WHITED are used for testing experiments to verify the effectiveness of the proposed method. The results indicate that the method has a high recognition accuracy, with average accuracies of 99.45% and 99.24% for the PLAID and WHITED datasets, respectively.