Due to the limitation of the receptive field of the shallow convolutional neural network (CNN) model,long distance features cannot be captured,and the spatial-spectral information of the image cannot be fully utilized in hyperspectral image classification,so it is difficult to obtain high precision classification results.To solve these problems,this paper proposes a model based on convolutional neural network and attention mechanism (CNNAM).The model uses coordinate attention (CA) to encode the position of the image channel data,and uses the Transformer module with self-attention mechanism as the core architecture to extract the long distance features to solve the CNN receptive field limitation problem.The overall classification accuracy of the proposed model on Indian Pines and Salinas datasets is 97.63% and 99.34%.Compared with other models,the proposed model shows better classification performance.In addition,the ablation experiments are carried out based on whether CA is combined with CNNAM,and it is proved that CA playes an important role in CNNAM.Experiments show that the combination of traditional CNN and attention mechanism can achieve higher classification accuracy in HSI classification.