To address the issue that the existing Knowledge Graph （KG） embedding models cannot adapt to the increasing KG， a dynamic KG continual embedding method based on gradient orthogonal projection， named GOPemb （Gradient Orthogonal Projection embedding）， was proposed. Firstly， the Core Gradient Spaces （CGSs） of old entities and old relationships were stored in historical snapshots during the training process. Secondly， when learning new triples， the gradient update directions for old entities and old relationships were constrained to align with the orthogonal directions of their respective CGSs， thereby learning new knowledge efficiently while preserving historical knowledge effectively. Finally， the CGSs of old entities and old relationships were updated to prepare for the next learning iteration. Experimental results show that compared to the best method in the comparison group， IncDE （Incremental Distillation Embedding）， GOPemb method achieves average improvements of 9.2%， 14.0%， and 8.0% in MRR （Mean Reciprocal Rank）， H@3 （Top-3 Hit Rate）， and H@10 （Top-10 Hit Rate）， respectively， on the selected datasets ICEWS05-15-CL、ICEWS18-CL and GDELT-CL. Furthermore， experimental results on learning efficiency confirm the time efficiency of GOPemb method， indicating that the method has efficient continual embedding capability.