Abstract: To address the irregularity and diversity of electricity consumption behavior among proxy power purchase users, this paper proposes a classification and recognition method based on multimodal hybrid features combining one-dimensional statistical features, Gramian Angular Field (GAF), and Recurrence Plot (RP), namely, 1D-GAFs-RP, to realize accurate identification of power consumer types. Initially, in accordance with the current time-of-use electricity price policy, the annual electricity consumption data of users are divided into four typical consumption curves corresponding to spring, summer, autumn, and winter. Additionally, considering the differences in electricity consumption behaviors between weekdays and holidays, the typical consumption curves for holidays are extracted. Subsequently, based on these five typical consumption curves, the statistical features such as users' price sensitivity coefficient and electricity consumption stability are calculated. Combining these with shape distances, the k-shapes clustering method is employed to generate user type labels. Finally, by applying GAFs and RPs to visualize the annual time-of-use electricity consumption curves and integrating them with statistical features, a multimodal hybrid feature-based user classification and recognition model is constructed. Experimental results indicate that the k-shapes clustering method based on shape distances can accurately classify user types. The proposed user type recognition scheme achieves an identification accuracy rate of over 94% for each cluster label, providing effective technical support for user-side management of proxy power purchase companies.