It is difficult to identify the fault features of rolling bearings under multiple working conditions. In this paper, one-dimensional multi-scale dense network (MSDNet) was applied to fault diagnosis of rolling bearings from the perspective of data-driven. Firstly, the time domain signal is used as the direct input of MSDNet to maintain the inherent characteristics of the signal. Secondly, three parallel convolution operations were used to extract multi-scale information inside the bearing fault signals. The addition of dense network prevented the loss of features in the process of information transmission, and alleviated the gradient disappearance problem in the model appropriately. Then, the Adabelief optimization algorithm is used to optimize the model parameters during the training process, which makes the model converge quickly and improve its generalization performance. Finally, confusion matrix and feature visualization were used to demonstrate the classification performance of the model. Several experiments have been carried out on Case Western Reserve University bearing datasets and Xi′an Jiaotong University datasets, and the fault recognition rate of the proposed algorithm can reach more than 98%, which proves the effectiveness of the proposed method.