Conventional　convolutional　neural　networks　(CNNs)　predominantly　emphasize　spatial　features　of　signals　and　often　fall　short　in　prioritizing　sequential　features.　As　the　number　of　layers　increases,　they　are　prone　to　issues　such　as　vanishing　or　exploding　gradients,　leading　to　training　instability　and　subsequent　erratic　fluctuations　in　loss　values　and　recognition　rates.　To　address　this　issue,　a　novel　hybrid　model,　termed　one-dimensional　(1D)　residual　network　with　attention　mechanism　and　bidirectional　gated　recurrent　unit　(BGRU)　is　developed　for　rotating　machinery　fault　classification.　First,　a　novel　1D　residual　network　with　optimized　structure　is　constructed　to　obtain　spatial　features　and　mitigate　the　gradient　vanishing　or　exploding.　Second,　the　attention　mechanism　(AM)　is　designed　to　catch　important　impact　characteristics　for　fault　samples.　Next,　temporal　features　are　mined　through　the　BGRU.　Finally,　feature　information　is　summarized　through　global　average　pooling,　and　the　fully　connected　layer　is　utilized　to　output　the　final　classification　result　for　rotating　machinery　fault　diagnosis.　The　developed　technique　which　is　tested　on　one　set　of　planetary　gear　data　and　three　different　sets　of　bearing　data,　has　achieved　classification　accuracy　of　98.5%,　100%,　100%,　and　100%,　respectively.　Compared　with　other　methods,　including　CNN,　CNN-BGRU,　CNN-AM,　and　CNN　with　an　AM-BGRU,　the　proposed　technique　has　the　highest　recognition　rate　and　stable　diagnostic　performance.