Remote sensing images can obtain a wide range of rich land surface data and details in a short time.The effective completion of remote sensing image scene classification has become an important basis for the analysis of relevant information.Based on this,a remote sensing image scene classification method based on BMFD NET algebraic model is proposed.In order to improve the classification accuracy of remote sensing images,Lie group algebraic model is introduced to analyze engineering problems to obtain low level features and reduce feature dimensions,and PS DenseNet is designed on the basis of DenseNet network to extract high level features.Then the focus cost function is used to complete the network training.The designed deep network model can train the features of massive remote sensing image samples,so the model has a strong self learning function,and the rich image features can be obtained automatically.In order to verify the effectiveness of the proposed method,two kinds of data sets were used to validate the proposed method and methods of reference［5］and ［6］. The experimental results show that the OCA parameters and Kappa parameters of the proposed method are better than those of the other two methods,and it could achieve a stable state with high classification accuracy in fewer epochs.