Graph　neural　network　(GNN)　has　the　proven　ability　to　learn　feature　representations　from　graph　data,　and　has　been　utilized　for　the　tasks　of　predicting　the　machinery　remaining　useful　life　(RUL).　However,　existing　methods　only　focus　on　a　single　graph　structure　and　cannot　integrate　the　correlation　information　contained　in　multi-graph　structures.　To　address　these　issues,　a　multi-graph　structure　GNN　prediction　method　with　attention　fusion　(MGAFGNN)　is　proposed　in　this　paper　for　GNN-based　bearing　RUL　prediction.　Specifically,　a　multi-channel　graph　attention　module　is　designed　to　effectively　learn　the　similar　features　of　node　neighbors　from　different　graph　data　and　capture　the　multi-scale　latent　features　of　nodes　through　the　nonlinear　transformation.　Furthermore,　a　multi-graph　attention　fusion　module　(MGAFM)　is　proposed　to　extract　the　collaborative　features　from　the　interaction　graph,　thereby　fusing　the　feature　embeddings　from　different　graph　structures.　The　fused　feature　representation　is　sent　to　the　long　short-term　memory　network　to　further　learn　the　temporal　features　and　achieve　RUL　prediction.　The　experimental　results　on　two　bearing　datasets　demonstrate　that　MGAFGNN　outperforms　existing　methods　in　terms　of　prediction　performance　by　effectively　incorporating　multi-graph　structural　information.