Hyperspectral imagery enhancing is very important to remote sensing interpretation. Recently, fusing hyperspectral data with its corresponding high spatial resolution multispectral data has been an important technology to obtain high-resolution imagery. Deep neural networks are widely used because of their significant performance, while they are still suffered from the problem of been insensitive to non-local information and the problem of overfitting. In this paper, a novel fusion method based on the non-local compressive network is proposed. This network can extract non-local information and also reduce overfitting when dealing with fusion tasks. Because of the introduction of non-local structure, the global relation information is also effectively enhanced according to the self-attention mechanism of features. Experiments on two datasets have shown that the proposed method can obtain better performance than the state-of-art methods.