A lightweight attention-guided super-resolution network (LAGNet) is proposed to address issues such as excessive computation and long training time caused by the redundant structure and increased parameters of image super-resolution reconstruction networks. First, the LAGNet introduces randomly initialized adaptive weights into the deep residual network structure to maximize the use of shallow feature information. Second, an attention guidance (AG) module uses the parallel structure of the efficient channel attention (ECA) model and the spatial group-wise enhance (SGE) model, combines the relationship between channels and the spatial location information characteristics, and employs the attention-guide layer to dynamically adjust the weight proportion of the two branches to obtain high-efficiency channel feature information. Finally, the global cascade connection is used to reduce network parameters and speed up information flow. The L1 loss function is used to accelerate convergence speed and prevent gradient explosion. The test results on the three benchmark datasets show that on average the peak signal-to-noise ratio of the LAGNet is increased by 0.39 dB, the model parameters are reduced by 24%, and the addition and multiplication operations are reduced by 62% compared with other networks; the overall visual effect of the image is clear and the detail texture is more natural.