We propose a hierarchical framework for psychological defense mechanism detection in multi-turn dialogues, integrating large language models, retrieval-augmented generation, and heuristic calibration. Our approach decomposes prediction into coarse-to-fine reasoning stages and incorporates dialogue reconstruction, explanation-enhanced retrieval, and hybrid LLM–supervised filtering to address severe label imbalance and implicit, context-dependent labeling. Experiments on the PsyDefDetect dataset show that LLM-based RAG improves performance on minority and ambiguous classes, achieving a Macro F1 of 0.31, while also revealing persistent challenges in fine-grained discrimination of latent psychological constructs.