Addressing the issue of high computational complexity and limited extraction of spatio-temporal features in 3D convolutional neural networks for video super-resolution tasks， this paper introduced a novel lightweight video super-resolution reconstruction network based on hybrid spatio-temporal convolution. Firstly， a hybrid spatio-temporal convolution-based module was proposed to realize the enhancement of the spatio-temporal feature extraction capability of the network as well as reduction of the computational complexity. Then， a similarity-based selective feature fusion module was proposed to further enhance the extraction capability of relevant features. Lastly， a motion compensation module based on the attention mechanism was designed to mitigate the effects of erroneous feature fusion to a certain extent. The experimental results demonstrate that the proposed network can achieve a favorable balance between video super-resolution performance and network complexity， and the 4-fold super-resolution reaches 8 frames per second on the benchmark dataset SPMCS-11. The proposed network meets the requirements for fast and accurate reasoning operations on edge devices.