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as the key technology of remote sensing, remote sensing image classification has been a hot spot of remote sensing research. In view of the current BP neural network model for remote sensing image classification, it is sensitive to the initial weight threshold, easy to fall into local extreme value and slow convergence speed, in order to improve the classification accuracy of BP model, the adaptive genetic algorithm is introduced into the parameter selection of BP network model. Firstly, the adaptive genetic algorithm is used to optimize the parameters of BP model weight threshold, then the improved BP algorithm is used to optimize the weight threshold of the optimized network, subsequently, a classification model of BP network based on adaptive genetic algorithm is established, and it is applied to the classification of remote sensing image data. The simulation results show that the new model can effectively improve the accuracy of remote sensing image classification, and put forward a new method for the classification of remote sensing image, which has a wide range of research value.