To solve the problem that the Transformer-based super-resolution network cannot fully utilize the surrounding information， a Mixed Attention Transformer image super-resolution network （MAT） based on neighborhood attention was proposed. Firstly， a convolutional layer was used to extract shallow features， and a series of Residual Mixed Attention Group （RMAG） and a 3×3 convolutional layer were used for deep feature extraction. In this way， the neighborhood attention and channel attention methods were combined， making full use of the complementary advantages of the two methods， that was the ability to utilize global statistics and strong local fitting simultaneously. In addition， an overlapping cross-attention module was introduced to enhance the interaction between adjacent window features. Secondly， a global residual connection was added to fuse shallow features and deep features. Finally， with pixel shuffling method adopted， the reconstruction module was used to upsample the fused features. Experimental comparison results of MAT and multiple algorithms such as RCAN （Residual Channel Attention Network）-it on multiple datasets show that the Peak Signal-to-Noise Ratio （PSNR） of the proposed algorithm is significantly higher than the advanced methods by 0.3 to 1.0 dB. It can be seen that MAT improves the image restoration effect in image super-resolution tasks effectively.