A computational approach to designing ideological and political education innovation courses via mobile cloud teaching

In the context of “Internet +” education, traditional teaching platforms often encounter challenges such as limited storage capacity, slow processing speed, and low operational efficiency. To address these issues, this study presents a novel computational framework for constructing an Ideological and Political (IAP) education platform leveraging Mobile Cloud Computing (MCC). The framework encompasses three key components: holistic architecture design, hardware optimization, and customized domain name configuration. Additionally, a K-means-based classification model tailored for Chinese text teaching resources (Trs) is proposed. This model employs the Vector Space Model (VSM) to represent textual features, and achieves clustering of IAP teaching resources through forward maximum matching segmentation and text similarity calculations. Experimental results demonstrate that the designed IAP platform significantly enhances the aggregation of high-quality teaching resources, improving operational efficiency by 32% and teaching synchronization rate by 28%. These findings highlight the platform’s potential as a scalable solution for exploring practical pathways in IAP innovation curriculum development, providing valuable references for computational education engineering.