By introducing the convolutional neural network (CNN) based classification algorithms,the accuracy of hyperspectral image (HSI) classification task has been significantly improved. However,the present mainstream CNN algorithms are usually complex and have a large number of parameters,which will make network hard to train and cause the over-fitting problem.To achieve lightweight on the premise of ensuring the performance of network,we proposed a lightweight CNN network based on spectral-spatial attention interaction mechanism for HSI classification task.To realize the spectral and spatial feature extraction,we constructed a lightweight dual-path backbone network for the extraction and fusion of the two kinds of features.And,to improve the representation ability of features,we designed two attention modules to adjust the weight of spectral and spatial features respectively.At the same time,to strengthen the correlation between two path features to achieve better fusion,attention interaction mechanism was introduced into the network to further improve the network performance. The classification results on three real HSI data sets show that the proposed network can achieve 99.5% classification accuracy and reduce at least 50% parameter amount compared with other networks .