Link prediction of knowledge graph is a relatively widely studied task in knowledge graph completion, the purpose of which is to complete the incomplete triples according to the original knowledge triples of the knowledge graph. To solve the problem of handling the heterogeneous neighborhood and the injective problem in the link prediction of knowledge graph, we propose a novel deep learning model called Transformation Assumptions with Message Passing Aggregation Network (TMPAN). TMPAN can effectively deal with the heterogeneous neighborhood information by introducing TransGCN’s transformation assumptions into DPMPN, which transforms head entities to tail entities using relationships as transformation operators. TMPAN also solves the injective problem caused by the single-aggregation operation by employing the multiple aggregators of the Principal Neighborhood Aggregation network (PNA) model. We comprehensively evaluate our model compared to typical baseline models by conducting experiments on two public datasets, FB15K-237 and YAGO3-10. The experimental results show the effectiveness of our model in the link prediction task.