Transformer method, relying on a self-attention mechanism, exhibits remarkable performance in the field of image super-resolution reconstruction. Nevertheless, the self-attention mechanism also brings about a very high computational cost. To address this issue, a lightweight image super-resolution reconstruction model based on a hybrid generalized Transformer is proposed. This model is built based on the SwinIR network architecture. Firstly, the rectangular window self-attention (RWSA) mechanism is adopted. It utilizes horizontal and vertical rectangular windows with different heads to replace the traditional square window pattern, integrating features across different windows. Secondly, the recursive generalized self-attention (RGSA) mechanism is introduced to recursively aggregate input features into representative feature maps, followed by the application of cross-attention to extract global information. Meanwhile, RWSA and RGSA are alternately combined to make more effective use of global context information. Finally, to activate more pixels for better recovery, the channel attention mechanism and self-attention mechanism are used in parallel to extract features from the input image. Test results of five benchmark datasets show that this model achieves better reconstruction performance while keeping the model parameters lightweight.