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In recent years, although some scholars have achieved satisfactory research results on hyperspectral image (HSI) classification, they often fail to achieve ideal classification results when facing small sample learning. Aiming at this problem, a hyperspectral image classification method was proposed by the organic combination of multi-attention mechanism fusion, compiled graph neural network and convolutional neural network. Firstly, a type of multiple mixed attention convolutional neural network (MCNN) and compiled graph neural network (CGNN) was designed, which can effectively retain the spectral and spatial information of HSI with limited learning samples; secondly, the introduced graph encoder and graph decoder can effectively map irregular HSI feature information; finally, the designed multi-attention mechanism can focus on some important HSI feature categories. In addition, the effect of different training samples on different algorithms for learning example classification was also investigated. Experiments on the public dataset Botswana (BS) showed that the proposed method improved the overall classification accuracy (OA) by 2.72 percentage points and 3.86 percentage points compared with the current state-of-the-art algorithms (CNN-enhanced graph convolutional network, CEGCN; weighted feature fusion of convolutional neural network, WFCG).Similarly, the experimental results on the IndianPines (IP) dataset with only 3% of the training sample data showed that the method also improved the OA of the current state-of-the-art algorithms (CEGCN and WFCG) by 0.44 percentage points and 1.42 percentage points, respectively. This demonstrated that the proposed method not only had good spatial and spectral information perception for HSI, but also showed strong classification accuracy with small learning data.