As an important component of mechanical system, rolling bearing is prone to failure due to harsh working environment. The vibration signal of faulty bearing includes transient impact components, harmonic components, background noise and other components. In order to extract fault features accurately, based on sparse representation theory, a bearing fault diagnosis method based on Laplace wavelet dictionary is proposed. First, a number of vibration signal fragments are intercepted, and the correlation filtering method is used to find the signal fragment with the largest correlation coefficient, and the basis function is determined accordingly, and Laplace wavelet atoms are constructed and expanded into a sparse dictionary. Then, the OMP algorithm is used to complete the sparse reconstruction of the signal under the dictionary to achieve noise reduction. Finally, the envelope analysis is performed on the noise reduction signal to extract the fault features and determine the fault type. Both simulation and experiment verify the effectiveness and feasibility of the proposed method, and it has certain application value.