Aiming at the problem that the interference of components with low correlation with fault features in the bearing vibration signal reduces the fault diagnosis accuracy, a bearing fault diagnosis method based on improved spectral kurtosis map and multi-dimensional fusion CNN is proposed. To improve the correlation between vibration signals and fault features and reduce interference components, an improved spectral kurtosis graph model was constructed based on DTCWPT to enhance the expression of multi-resolution differential fault features. Then, considering the rich feature dimension, a multi-dimensional fusion CNN model is constructed, and the original signal and the improved spectral kurtosis map are used as input together. The experimental results show that the method can extract different fault features in the vibration signals of various types of bearings, and can accurately identify bearing faults under multiple working conditions, with good diagnostic accuracy.