Aiming at the problem that the classification accuracy of hyperspectral images is not ideal when the amount of training samples of three-dimensional convolutional network is limited, an efficient classification model based on multi-feature fusion and hybrid convolutional neural networks is proposed in this paper. First, after the dimensionality reduction processing is performed on hyperspectral images, the three-dimensional convolutional layer is used to extract deep hierarchical spatial-spectral joint features. Then, the residual connection is introduced to perform multi-feature fusion through feature map concatenation and pixel-wise addition to realize feature reuse and enhance information transmission. Finally, a two-dimensional convolutional layer is used to enhance the spatial information of the extracted features and realize image classification. The experimental results show that in the three publicly available hyperspectral data sets Indian Pines, Salinas and University of Pavia, 5%, 1% and 1% of the labeled samples are used as training data, respectively, the classification accuracy of the model is 97.09%, 99.30% and 97.60%, respectively, which can effectively improve the classification accuracy of hyperspectral images for under small sample condition.