Core image is one of the most important geological data in the process of oil and gas field development, it is of great significance to understand the underground geological condition, determine the core lithology and infer the sedimentary environment. Aiming at the problems such as excessive consumption of computing resources, easy overfitting of models, image details are lost and pixel redundancy, a high resolution network core image segmentation based on multi-scale and context information was proposed. Due to the parallel superposition of the pyramidal module network, the feature information cannot be shared among branches. The pyramidal module was improved into a network model with skip connection structure, which makes certain connections between the cavity convolution with different expansion rates and forms a denser network structure. Then the improved enhanced atrous spatial pyramid pooling (EASPP) module was introduced into each subnet of the high-resolution net (HRNet), which solves the problem of image pixel redundancy in HRNet method, and can enlarge the receptive field and capture multi-scale context information. The experimental results show that compared with the current mainstream semantic segmentation algorithms, this method has different degrees of improvement in mean intersection over union(MIoU) and pixel accuracy(PA) evaluation indicators, and it is better to deal with over-segmentation or under-segmentation, and retains image details to the greatest extent.