Fault diagnosis of beam chopper bearings is essential for the safe and stable operation of accelerator systems. However, variations in rotational speed cause substantial shifts in vibration data distributions, making it difficult for existing data-driven models to generalize to unseen operating conditions. This paper proposes a language-distilled hierarchical mechanism reasoning network (LDMR-Net) for cross-condition bearing fault diagnosis. First, a large language model is used to extract diagnostic knowledge from source-domain samples and organize it into evidence-phenomenon-mechanism (E-P-M) knowledge graph. A dynamic retrieval module then links vibration features to relevant graph evidence and performs hierarchical reasoning along the E-P-M path. Reasoning distillation and invariance constraints further promote consistent representations of the same fault across different speeds. Experiments on a beam chopper bearing test rig at 200, 400, and 600 r/min show that LDMR-Net achieves average accuracy of 94.64%, demonstrating its effectiveness in cross-domain fault diagnosis.