Traditional convolutional neural network models fail to fully utilize the rich spatial-spectral information in high-resolution hyperspectral images, and have the problems of high computational cost and low classification accuracy for small sample data. This study proposes a lightweight multiscale pyramid hybrid pooling hybrid convolution model. Based on the hybrid convolution network, the proposed model uses an improved pyramid pooling module to enhance the ability to extract spatial-spectral features, uses fewer convolution layers and depth separable convolution, and uses the global average pooling layer to replace a part of the full connection layer to achieve the transition from the convolution layer to the full connection layer, significantly reducing number of parameters. In this study, the proposed method is tested on three high-resolution hyperspectral datasets and compared with classical hyperspectral image classification methods. The results show that the proposed method can achieve the best classification results under high-resolution conditions, multiple ground object types, and complex boundaries. The overall accuracy of the proposed method on WHU-Hi-LongKou, WHU-Hi-HanChuan, and WHU-Hi-HongHu datasets are 99.12%, 98.43%, and 98.84%, respectively, when only 1%, 2%, and 2% training samples are used, which is superior to that of the traditional convolutional networks. It is proved that the model proposed in this study has a low computational cost, high accuracy for small sample problems, and can be well applied to high-resolution hyperspectral datasets.