Hyperspectral image classification methods based on the classical convolutional neural network (CNN) have some problems,such as insufficient expression of key detail features and a large number of samples for training.Aiming at these problems,this paper proposes a hyperspectral image classification model with multi-scale features and dual-attention mechanism.Firstly,using 3D convolution,the spatial-spectral features of images can be directly extracted,and transposed convolution is adopted to get more details of the feature map.Then,a feature extraction module is built through convolution kernels of different sizes to achieve multi-scale feature fusion under different receptive fields.Finally,the dual-attention mechanism is designed to suppress the confused regional features and highlight the distinguishing features.The experimental results on two hyperspectral images show that when 10% and 0.5% samples are randomly selected as training samples for each class of ground object,the overall classification accuracy of the proposed model is improved to 99.44% and 98.86%,respectively.This model can obtain higher classification accuracy than some mainstream deep-learning classification models.Since the model can focus on more important detailed features during feature extraction,the classification effect is improved.