Hyperspectral remote sensing，which plays an important role in the field of earth observation and remote sensing,could be used to obtain more accurate and rich remote sensing information,thus covering various levels and full links of the various countries′ aerial,spaceflight and small range of ground observation.However,hyperspectral data sets are often very large and contain redundant information,which brings inconvenience to subsequent processing.In this study,Laplacian Eigen mapping was used to reduce the dimension and fulfil the feature extraction of hyperspectral data.Then a weighted naive Bayes classification algorithm was proposed,while the classic naive Bayes classifier was improved by the method of rewarding weight.The algorithm was verified by the open source data.Results indicated that the accuracy for the proposed method in identification of the object information reached to 92.7%,which was greatly improved compared with that for the traditional method.