In recent years, data-driven methods have been widely used in rolling bearing fault diagnosis with great success, which mainly relies on the same data distribution and massive labeled data. However, bearing equipment is in normal working state for most of the time and operates under variable operating conditions. This makes it difficult to obtain bearing data labels, and the distribution of the collected samples varies widely. To address these problems, an unsupervised cross-domain fault diagnosis method based on time-frequency information fusion is proposed in this paper. Firstly, wavelet packet decomposition and reconstruction are carried out on the bearing vibration signal, and the energy eigenvectors of each sub-band are extracted to obtain a 2-D time-frequency map of fault features. Secondly, an unsupervised cross-domain fault diagnosis model is constructed, the improved maximum mean discrepancy algorithm is used as the measurement standard, and the joint distribution distance is calculated with the help of pseudo-labels to reduce data distribution differences. Finally, the model is applied to the motor bearing for comparison and verification. The results demonstrate its high diagnosis accuracy and strong robustness.