In the field of load identification, it is difficult to effectively distinguish loads with similar trajectories using a single load feature. To address this issue, a non-intrusive load identification method based on multiple V-I (voltage-current) trajectory fusion is proposed. This method first preprocesses high-frequency sampling data to extract the fundamental voltage (V₁), fundamental current (I₁), and maximum harmonic current (I_{h max}). Subsequently, the fundamental voltage is combined with the fundamental current and maximum harmonic current to construct V₁-I₁ trajectories and V₁-I_{h max} trajectories. Finally, these two trajectory features are input into a two-dimensional convolutional neural network (2DCNN) for load classification. Validation using the public PLAID and WHITED datasets shows that the proposed load identification method achieves accuracies of 99.66% and 99.81%, respectively. These results indicate that the proposed method not only enriches the information used for classification but also significantly improves load identification accuracy, demonstrating its practical application value in power monitoring and load management.