In online education engineering, video-image transmission under bandwidth constraints and latency issues often leads to resolution degradation, significantly undermining the learning efficacy of ideological and political (IAP) courses. Addressing this critical engineering challenge requires computational solutions tailored to educational video processing demands. This study proposes a dedicated computational framework integrating Multi-Processor Unit (MPU) architecture and local image matching for super-resolution (SR) enhancement in IAP course materials. The framework leverages MPU-based parallel computing to optimize hierarchical image feature extraction, with course-specific slide materials as guidance signals for efficient low-frequency feature mining. Local image matching is applied to restore high-frequency details in teaching videos by harnessing spatial-temporal contextual information, aligning with the real-time processing needs of online education systems. Experimental validation on Vid4, Udm10, and a custom IAP online course dataset demonstrates practical engineering advantages: 42% higher frame rate in feature extraction, 37% improvement in visual quality (PSNR/SSIM), and real-time 60fps processing for 1080p streams— meeting the stringent technical requirements of educational video platforms. This framework not only enhances the visual quality of IAP online courses but also provides a reproducible computational paradigm for educational technology engineering, supporting the digital transformation of ideological and political education through practical computational methods.