Graph　neural　network　(GNN)　is　an　effective　tool　for　semi-supervised　fault　diagnosis　of　rotating　machinery.　However,　existing　GNN　based-semi-supervised　methods　only　rely　on　single　graph　structure　to　learn　feature　representation　under　limited　labeled　samples,　while　the　information　of　different　topology　graph　structures　cannot　be　directly　fused　due　to　the　large　difference　of　feature　extracting,　leading　to　insufficient　node　relationships　and　label　information　mining.　Besides,　static　or　limited　dynamic　feature　extraction　of　neighbor　nodes　will　hinder　the　expressiveness　of　semi-supervised　GNN　models.　To　overcome　these　limitations,　a　dual　graph　drivenconsistent　representation　learning　method　(DGDCRL)　is　proposed　in　this　paper.　First,　a　dual　graph　structure　with　two　different　topology　graphs　is　conducted　using　graph　label　passing　method,　in　which　limited　labeled　sample　information　are　fully　leveraged　and　richer　topology　structure　information　among　nodes　can　be　captured.　Second,　a　consistent　representation　learning　method　with　gated-dynamic　enhanced　graph　attention　module　(GDEGAT)　is　proposed　to　extract　the　common　embeddings　from　two　topology　graphs,　where　a　DEGAT　layer　is　developed　to　aggregate　neighbor　information　more　dynamically　and　expressively.　Besides,　to　enhance　the　alignment　between　the　embeddings　of　the　same　nodes　across　two　topology　graphs,　we　design　a　consistent　representation　loss.　Two　datasets　are　used　to　validate　the　performance　of　the　proposed　method,　indicating　that　the　proposed　DGDCRL　method　with　GDEGAT　module　can　achieve　the　effective　diagnosis　results　of　rotating　machinery　under　both　constant　and　variable　speed　conditions,　and　the　DGDCRL　method　can　effectively　enhance　the　semi-supervised　diagnostic　ability　of　baseline　GNNs　under　low　labeled　rates.