Knowledge　graph　completion　(KGC)　aims to　enhance　the　completeness　and　utility　of　knowledge　graphs　(KGs) by　predicting　and　filling　missing　information.　Existing　methods　primarily　focus　on　structured　representation　learning,　extracting　low-dimensional　embeddings　of　entities　and　relations　to　uncover and　predict　missing　information　in　knowledge　graphs.　However, these　methods　often　overlook　entity　type　information　and　lack　deep　feature　extraction　capabilities.　Inability　to　recognize　the　type　information　of　entities　may　lead　to　poor　embedding　expression　effects of　entities,　while　insufficient　deep　feature　extraction　limits the　model’s　ability　to　understand　complex　relationships.　To　address　these　issues,　this　paper　proposes　a　Knowledge　Graph　Completion with　Entity　Type-Aware　and　Deep　Feature　Extraction　(TAFE).　The model　employs　a　Type-Aware　Graph　Attention　Encoder　(TA-GAT)　to　identify　equivalence　relations　and　model　entity　type　information　during graph　context　entity　aggregation.　Additionally,　it　incorporates　a Deep　Feature　Extraction　3D　Convolution　Decoder　(FE-Conv3D), using　Gaussian　function　mapping　techniques　to　capture　deep　feature　information　of　entities　and　relations.　The　3D　convolutional　kernels　extract　interaction　and　local　features　among　embeddings,　enhancing　the　model’s　ability　to　capture　details　and　understand　complex　relationships.　Extensive　experimental　analysis　demonstrates the　effectiveness　of　TAFE　in　knowledge　graph　completion　tasks.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.