In order to fully segment and classify the artistic objectives of painting and realise the needs of automatic classification and retrieval of painting by computer, this paper proposes to establish convolution neural network with dual core compression activation module and deep separation convolution. The DKSE module is constructed based on the structural features of SKNet. SKNet extracts the overall image and detail features, SENet enhances the channel features. Using DKSE module and depth separable convolution, a convolution neural network is established to classify paintings. DKSE module can effectively improve the classification performance of the model, fully extract the overall and local detail features of oil painting images, and provide better classification accuracy than the traditional network model.