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 Principal Neighborhood Aggregation network (PNA) model. We comprehensively evaluate our model compared to baseline models on typical public datasets.