The state of rolling bearings has a great influence on the working state of the whole machine, but the fault diagnosis method of the rolling bearings at present has some problems, such as dependency on manual feature extraction and low robustness. Therefore, we propose a fault diagnosis method of rolling bearings (1D-CNN-LSTM) based on the improved integration of 1D Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) network. Firstly, the 1D-CNN-LSTM model is used to classify and identify six different working states of rolling bearings. The experimental results indicate that the proposed classification model can identify different states of rolling bearings at a high speed, with an average identification accuracy of 99.83%. Secondly, the proposed model is compared with some traditional algorithm models and shows great advantages in measuring accuracy. Finally, transfer learning is introduced to test the robustness and generalization ability of the proposed model. The experimental results demonstrate that the model proposed in this study has good adaptability and high efficiency under different working conditions, featuring strong generalization ability and engineering application feasibility.