[Objective] The existing few-shot knowledge graph completion methods could not distinguish the importance of neighbors when dealing with complex relations, which resulting in low performance of entity prediction. The few-shot knowledge graph completion methods could be improved by considering entity neighbor information. [Methods] First, we use a type-aware neighbor coder to learn the implicit type information in the entity neighbor such that the type-aware attention can be obtained and the entity representation can be enhanced. Then, a Transformer encoder is used to capture different meanings of a task relation. Finally, the reference set representation is obtained by jointly matching the prototype network aggregation and predicting the entity. [Results] The proposed method is verified on NELL and Wiki datasets through entity prediction tasks. The results show that the MRR is 1.6% and 1.2% higher than the baseline methods on the two datasets, respectively. [Limitations] Neighbors with lower physical relevance were not filtered, and the noise would affect the distribution of type-aware attention weights. [Conclusions] Experimental results show that the proposed method improves the few-shot knowledge graph completion performance by learning more abundant entity neighbor information.