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In view of the existing CNN（Convolution Neural Network） based rolling bearing fault diagnosis method，it is difficult to effectively excavate and utilize the multi-scale information contained in the data，a multi-scale convolution feature fusion method for rolling bearing fault diagnosis is proposed. A convolutional neural network （MSCNN） structure with multi-scale feature extraction and fusion ability is built by adding the upper sampling layer，which improves the understanding ability of the model to the input signal. CWRU database is used to verify the validity of the proposed method. The short-time Fourier transform is used to analyze the spectrum of rolling bearing signals. The spectrum samples are input into MSCNN network. The data analysis shows that the method can effectively improve the fault diagnosis accuracy.