The fault diagnosis is an effective technical means to improve the reliability of centrifugal fan bearings. The serious imbalance of data is one of the important issues facing bearing fault diagnosis.In this paper, a transfer learning-based fault diagnosis method for Centrifugal fan bearings is proposed, utilizing the improved CNN (I-CNN) and Joint Maximum Mean Discrepancy (JMMD) algorithms. The raw vibration signals of the bearings are enhanced through fast Fourier transform for feature representation. The signals are then processed by parallel multi-scale CNNs with an embedded Squeeze-and-Excitation (SE) attention to focus on key features. Furthermore, the JMMD is introduced as a metric for quantifying the disparity between the source and target domains, thereby mitigating domain shift. In the loss function, weight factors and scaling factors are introduced to increase attention on minority samples and easily confused samples within the imbalanced dataset. The proposed method is validated on the Centrifugal fan bearing dataset from Jiangnan University and the CWRU dataset.