To solve the problems of the conventional rolling bearing fault diagnosis method requiring a large amount of prior knowledge and easy to introduce error artificially, this paper presents a DenseNet-BLSTM method for rolling bearing fault diagnosis. The method combines the superiority of the multi-scale deep feature extraction ability of the densely connected convolutional network (DenseNet) and the advantage of bidirectional long short-term memory (BLSTM) in sequence modeling. First, multiscale abstract features are extracted from the vibration signal of a rolling bearing using one-dimensional convolution kernels. Then, the BLSTM was used to learn the time-dependence of features. Finally, the feature information is mapped to corresponding fault modes by the fully connected layers. The experimental results show that the accuracy of the proposed method is 99.5% in multi-load scenarios, and the method has good load adaptability and anti-disturbance ability.