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Based on convolutional neural networks， this paper proposes innovative ideas for diagnosing intracranial tissue and organ diseases. The images of Parkinson’s disease patients were preprocessed by gray mapping， histogram equalization， improved wavelet denoising and image enhancement. A convolutional neural network is designed and built on the basis of VGG-Net network model. Regularization strategy is adopted to avoid over-fitting problem. Then the ten-fold cross-validation of the network model is carried out five times with MRI image set of patients. And feature visualization is performed by deconvolution network to dig out latent features of diseases. The result and objective evaluation show that the network can make a good diagnosis of MRI image for Parkinson’s disease. [ABSTRACT FROM AUTHOR]