A dual-channel graph neural network method was proposed for user-level fraud detection tasks on E-commerce platforms to address the limitations of existing approaches that overemphasized global modeling of user behavior while insufficiently exploiting comment information. Multi-dimensional user behavior was modeled through the construction of two complementary graphs: an entity interaction graph and a comment semantic graph. The entity interaction graph was designed to capture global interaction patterns based on purchase and rating behaviors, while the comment semantic graph was built to model time-sensitive semantic relations between comments for characterizing fine-grained behavioral features. Parallel modeling of the dual graphs was performed using graph neural networks. Dynamic interaction optimization between dual-channel features was achieved through an attention mechanism, and higher-order node features containing multi-hop neighborhood information were generated. A comprehensive user-level behavior representation was produced by adaptively fusing different neighborhood ranges and feature spaces with a multi-head additive attention mechanism. Experimental evaluations were conducted on public datasets to validate the proposed method, and significant improvements were observed in multiple evaluation metrics compared to traditional approaches. Results show that the proposed method effectively enhances fraud detection performance at the user level.