Aiming at the problem that the early fault features of rolling bearings are weak and difficult to be effectively identified, this paper proposes a fault diagnosis method for rolling bearings which is based on tSNE-ASC feature selection and DSmT fusion decision. Multiple sensors were used to collect bearing acoustic signals under different fault modes, and each signal was decomposed by VMD to obtain multiple IMF components. Feature extraction was carried out for each IMF component, and data set matrix of each feature was constructed. TSNE was used to reduce the dimension of the matrix of each feature data set to two dimensions and calculate the average contour coefficient (ASC). According to the fact that ASC greater than critical value, the sensitive features of acoustic fault signal are extracted. The primary diagnosis of bearing fault is realized based on diagnosis model. DSmT is used to fuse the primary diagnosis result of acoustic signal and get the final diagnosis conclusion. Experimental results show that the tSNE-ASC feature selection method can effectively extract sensitive features in the mixed domain, and has high diagnostic accuracy in different working conditions and different diagnostic models. DSmT decision fusion effectively reduces the uncertainty of single signal diagnosis, and has high diagnostic accuracy under the condition of variable load and non-stationary speed up and down.