The invention relates to a neural network framework for remote sensing scene classification and a classification method, in particular to a multi-task neural network framework for remote sensing sceneclassification and a classification method, and solves the problems of limitation of information amount, inaccurate scene recognition and low classification precision of existing network frameworks and classification methods. The network framework comprises a convolution feature extraction layer, a classification task full-connection feature extraction layer, a classification task discriminationlayer and a classification task loss layer; the network framework is characterized by further comprising an auxiliary task full-connection feature extraction layer, an auxiliary task discrimination layer, an auxiliary task loss layer, a classification task feature mapping layer, an auxiliary task feature mapping layer and a relationship learning loss layer. Wherein the two feature mapping layers respectively carry out dimensionality reduction on full-connection feature vectors adapted to two tasks, the relation learning loss layer carries out subtraction on the vectors after dimensionality reduction and takes norms of difference vectors as relation learning losses, and the relation learning losses and discrimination losses of the two tasks are added into optimization training together.