Abstract This study explores the integration of the MOOC (Massive Open Online Course) model into physical education (PE) instruction at ordinary Chinese universities within the "Internet+" context. The author identifies limitations in traditional PE instruction such as overreliance on demonstration based teaching, insufficient theoretical practical integration, and heightened injury risks and proposes the MOOC framework as a modern, technology enhanced alternative. An empirical experiment was... This study explores the integration of the MOOC (Massive Open Online Course) model into physical education (PE) instruction at ordinary Chinese universities within the "Internet+" context. The author identifies limitations in traditional PE instruction such as overreliance on demonstration based teaching, insufficient theoretical practical integration, and heightened injury risks and proposes the MOOC framework as a modern, technology enhanced alternative. An empirical experiment was conducted with 80 volleyball students divided into control (traditional teaching) and experimental (MOOC-based teaching) groups over 112 days. Results showed that the MOOC group significantly outperformed the control group in theoretical knowledge and skill proficiency (p < 0.05), though no significant difference was found in physical fitness. The study further applied the K-means clustering algorithm to analyze comprehensive training effectiveness, confirming superior outcomes in the MOOC group. The research underscores the potential of MOOC systems to enhance PE delivery by offering structured, precise, and engaging content, thereby supporting national goals of improving public health and elevating China's international sports profile. Despite limitations like a small sample size, the findings provide a theoretical and practical foundation for reforming PE pedagogy through digital innovation in higher education. Read More Read Less

Abstract The paper explores the application of multi channel stress management intervention technology to improve college students' mental health within China's educ- ational context. It critiques traditional, static mental health education models as outdated and inefficient, arguing for the integration of computer technology and big data analytics to modernize mental health support. The proposed model leverages data mining, filtering algorithms, and cross platform data collection... The paper explores the application of multi channel stress management intervention technology to improve college students' mental health within China's educ- ational context. It critiques traditional, static mental health education models as outdated and inefficient, arguing for the integration of computer technology and big data analytics to modernize mental health support. The proposed model leverages data mining, filtering algorithms, and cross platform data collection to tailor interventions to individual student needs. A key methodological component involves the "three line translation theory" to optimize parameter settings and enhance algorithmic precision in psychological course delivery. The study conducted a trial at a major university, comparing a control group receiving conventional instruction with an experimental group receiving the new intervention model. Results showed significantly higher mental health scores and improved learning efficiency in the experimental group, as detailed in performance tables. The research underscores the potential of technology driven, adaptive mental health frameworks to align with evolving student cognitive patterns and educational reforms. Despite promising outcomes, the paper acknowledges limitations, particularly delays in capturing students' real time psychological states during retesting. Overall, the study advocates for a systemic shift toward data informed, dynamic mental health education strategies grounded in multi channel intervention technologies, aiming to enrich teaching materials and improve student wellbeing in higher education. Read More Read Less

Abstract With the rapid growth of unstructured data particularly in archival and document management systems traditional data processing methods have become inadequate for extracting meaningful knowledge. This paper addresses the challenge by integrating BP neural networks and genetic algorithms to enhance data mining capabilities on heterogeneous, text-based archival records. The authors propose a data warehouse architecture using a star schema to organize document metadata, including... With the rapid growth of unstructured data particularly in archival and document management systems traditional data processing methods have become inadequate for extracting meaningful knowledge. This paper addresses the challenge by integrating BP neural networks and genetic algorithms to enhance data mining capabilities on heterogeneous, text-based archival records. The authors propose a data warehouse architecture using a star schema to organize document metadata, including filing year, unit, type, and content, while tackling pervasive data quality issues such as missing values, inconsistent cataloging standards, and formatting errors. Data cleaning is performed using SQL Server Integration Services (SSIS), and text features are represented using TF-IDF for improved neural network input representation. The study highlights that a significant portion of archival data is underutilized due to poor standardization and incomplete metadata, which hampers effective mining. Preliminary results suggest that intelligent data preprocessing combined with optimized neural networks can uncover latent patterns in document usage and improve archival decision making. The work lays the foundation for future research in automated, intelligent archival systems, though challenges remain in data integration, standardization, and model refinement. Read More Read Less