Deep　learning　has　been　an　important　topic　in　fault　diagnosis　of　motor　bearings,　which　can　avoid　the　need　for　extensive　domain　expertise　and　cumbersome　artificial　feature　extraction.　However,　existing　neural　networks　have　low　fault　recognition　rates　and　low　adaptability　under　variable　load　conditions.　In　order　to　solve　these　problems,　we　propose　a　one-dimensional　fusion　neural　network　(OFNN),　which　combines　Adaptive　one-dimensional　Convolution　Neural　Networks　with　Wide　Kernel　(ACNN-W)　and　Dempster-Shafer　(D-S)　evidence　theory.　Firstly,　the　original　vibration　time-domain　signals　of　a　motor　bearing　acquired　by　two　sensors　are　resampled.　Then,　four　frameworks　of　ACNN-W　optimized　by　RMSprop　are　utilized　to　learn　features　adaptively　and　pre-classify　them　with　Softmax　classifiers.　Finally,　the　D-S　evidence　theory　is　used　to　comprehensively　determine　the　class　vector　output　by　the　Softmax　classifiers　to　achieve　fault　detection　of　the　bearing.　The　proposed　method　adapts　to　different　load　conditions　by　incorporating　complementary　or　conflicting　evidences　from　different　sensors　through　experiments　on　the　Case　Western　Reserve　University　(CWRU)　motor　bearing　database.　Experimental　results　show　that　the　proposed　method　can　effectively　enhance　the　cross-domain　adaptive　ability　of　the　model　and　has　a　better　diagnostic　accuracy　than　other　existing　experimental　methods.