Remote sensing image has great application prospect in multiple fields, while classification is the key for utilization of these images. Meanwhile, the recent growth in edge computing eases the high hardware requirement of machine learning methods. In order to improve the existing problems of accuracy dissatisfactory and high data dependency in remote sensing image classification, this manuscript establishes a semi-supervised contrastive learning image classification neural network model based on SimCLR algorithm suiting for edge computing. The semi-supervise network architecture is conductive to obtain optimal value with unlabeled data, which is important due to the inadequacy of labeled remote sensing data. By incorporating an interpolation mix module for feature space expression optimization, the proposed model improves the sample utilization ability and speeds up the convergence rate of the network. In this manuscript, the local feature contrastive learning module extracts secondary features, for the sake of enhancing the classification accuracy. The experiment outcomes demonstrate that our model has comparatively good performance on remote sensing image datasets and better handling for data insufficiency.