The goal of multilingual knowledge graph completion (MKGC) is to improve link prediction performance on the target knowledge graph by leveraging data from other language-specific knowledge graphs. Existing methods usually use pre-aligned entities between different knowledge graphs to accomplish knowledge transfer. However, there are usually no pre-aligned entities between different knowledge graphs in practical scenarios, making knowledge transfer difficult to achieve. Considering the MKGC without aligned entity pairs, a pseudo-aligned entity generation module that integrates a pre-trained language model is proposed to iteratively generate new aligned entities for knowledge transfer. It is suggested to use a graph neural network based on multi-graph attention (MGA-GNN) to encode the triples in order to differentiate the information in various language-specific wisdom graphs. Finally, the plausibility of the triples is calculated via the embeddings output by the network to conduct the link prediction task. Experimental results on the DBP-5L and E-PKG datasets show the effectiveness of the proposed method and its superior performance in more practical scenarios.