<p>Currently, most video super-resolution (VSR) models primarily employ single-branch frameworks, which cannot simultaneously balance efficiency and accuracy. If VSR employs neural networks with fewer parameters to improve device portability, the network cannot effectively handle complex consecutive frames with significant variations. Therefore, we propose a computational accelerated VSR network based on inter-frame information complexity classification (IFIC3A-VSR). IFIC3A-VSR consists of Frame Value Classification Module (Class Module), which classifies the information in consecutive frames and refines the VSR task, and Video Super-Resolution Multi-Branch Module (VSR Module), which handles various super-resolution (SR) tasks effectively. Additionally, we introduce a new convolutional unit called Self-Calibrated deformable 3D convolution (SCdcn), which processes texture information based on contextual information at each spatial position, enriching the overall feature structure. Moreover, we construct two lightweight attention mechanisms for each branch to assist in processing the corresponding SR tasks. Experimental results, analyzed through subjective visual evaluation and objective performance metrics, demonstrate the effectiveness of the IFIC3A-VSR. The IFIC3A-VSR achieves outstanding performance and reduces computational load by 35% compared to mainstream algorithms.</p>