In practical industrial scenarios， the significant divergence in bearing state evolution caused by complex operating conditions imposes dual constraints on bearing life prediction： incomplete mechanism understanding and distribution shift in monitoring data. Although the existing data-driven methods perform well under stable conditions， they suffer from strong reliance on annotated data and limited generalization capability under rappidly changing conditions. To address these issues，a method for Bearing Life Prediction based on Dynamic Knowledge embedding （DK-BLP） was proposed in this paper. The method dynamically integrated data-driven features and domain prior knowledge. Firstly， a dynamic knowledge graph was constructed， the prior knowledge of bearing degradation was encoded into computable triplets， and a sliding window confidence mechanism was introduced， so as to realize adaptive updating of domain knowledge. Second， relational graph convolutional networks were used to extract physically meaningful embedding vectors， which were then fused cross-modally with time-frequency features of vibration signals extracted by hierarchical convolutional networks. Finally， dynamic interactions between features and knowledge were modeled by using a multi-head self-attention Transformer， thereby allowing the model to adaptively balance the contributions of data features and mechanistic knowledge. Experimental results on the PHM2012 and XJTU datasets demonstrate that the proposed method significantly improves the accuracy of cross-condition bearing life prediction.