With　the　development　of　information　technology,　machine　translation　technologies　play　a　crucial　role　in　cross-language　communication.　However,　there　is　a　problem　of　information　loss　in　machine　translation.　In　view　of　the　common　problem,　this　paper　proposes　three　Transform-Information　Combination　(Transformer-IC)　models　based　on　information　combination　method.　The　models　are　based　on　the　Transformer　and　select　different　middle-layer　information　to　compensate　the　output　through　arithmetic　mean　combination　method,　linear　transformation　method　and　multi-layer　information　combination　method　respectively.　Experimental　results　based　on　Linguistic　Data　Consortium　(LDC)　Chinese-to-English　corpus　and　International　Workshop　on　Spoken　Language　Translation　(IWSLT)　English-to-German　corpus　show　that　the　BLEU　values　of　all　kinds　of　Transformer-IC　model　are　higher　than　that　of　the　reference　model,　in　particular　the　arithmetic　mean　combination　method　improves　the　BLEU　value　by　1.9.　Compared　with　the　Bert　model,　the　results　show　that　even　though　the　Bert　model　has　a　good　performance,　the　Transformer-IC　models　are　better　than　the　Bert　model.　Transformer-IC　models　can　make　full　use　of　the　middle-layer　information　and　effectively　avoid　the　problem　of　information　loss.　©　2021　IEEE