As a powerful visual model, convolutional neural networks (CNNs) have demonstrated remarkable performance in various visual recognition problems, and attracted considerable attention in recent years. However, due to the highly correlated bands and insufficient training samples of hyperspectral image data, it still remains a challenging problem to effectively apply the CNN models on hyperspectral images. In this paper, an efficient CNN architecture has been proposed to boost its discriminative capability for hyperspectral image classification, in which the original data is used as the input and the final CNN outputs are the predicted class-related results. The proposed CNN infrastructure has several distinct advantages. Firstly, different from traditional classification methods those need hand-crafted features, the CNN model used here is designed to deal with the problem of hyperspectral image analysis in an end-to-end way. Secondly, the parameters of the CNN model are optimized from a small training set, while the over-fitting problem of the neural network has been alleviated to some extent. Finally, in order to better deal with the hyperspectral image information, 1x1 convolutional layers have been adopted, and an average pooling layer and larger dropout rates have also been employed in the whole CNN procedure. The experiments on three benchmark data sets have demonstrated that the proposed CNN architecture considerably outperforms other state-of-the-art methods.