Abstract: To reduce the redundancy in hyperspectral images and further explore their potential classification information, a convolutional neural network(CNN) classification model based on feature importance is proposed. First, the random forest(RF) model obtained by Bayesian optimization training is used to evaluate the importance of hyperspectral images. Second, an appropriate number of hyperspectral image bands are selected as new training samples according to the evaluation results. Finally, the 3D-CNN is used to extract and classify the obtained samples. Based on two sets of measured hyperspectral remote sensing image data, the experimental results demonstrate the following: compared with the original spectral information obtained directly using a support vector machine(SVM) and the CNN classification effect, the proposed hyperspectral classification model based on feature importance can effectively improve the classification accuracy of hyperspectral images while reducing dimensionality.