Vehicle images collected by the existing traffic monitoring terminals often have low resolution in distant view， accompanied by uncertain pixel influencing factors such as strong noise， blur， overexposure， and underexposure， making it difficult to ensure accuracy of intelligent recognition of license plate information. In response to the above issue， Super-Resolution reconstruction for Low-quality License plate information based on multi-dimensional spatial convolutional information enhancement （LL-SR） network was proposed. Firstly， the correlation of feature points in space and channels mined by convolution were used to aggregate shallow feature. Secondly， correlation between feature maps was mined from different receptive fields and different dimensions， so as to recover high-frequency details of license plate information. Finally， the obtained features of different scales were fused and corrected at pixel level across channels to reduce propagation of useless features in context， thus achieving super-resolution reconstruction of low-quality license plate information. Experimental results on License plate of Taiyuan （LT） and License plates of the United States of America （LU） datasets show that the Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） of the proposed network are 26.682 4 dB， 0.820 3 and 22.356 7 dB， 0.781 3 respectively， which are improved by 0.210 9 dB， 1.736 1 dB； 0.005 7， 0.033 0； and 0.472 8 dB， 1.419 2 dB； 0.019 6， 0.039 9 respectively compared to those of NGramSwin （N-Gram in Swin transformers） and CARN （CAscading Residual Network）. Moreover， the license plate information reconstructed by the proposed network has better visual effects.