Integration of algorithms in educational decision support systems and analysis of environmental adaptability

This research focuses on the issues of algorithm integration and environmental adaptability in educational decision support systems. The study proposes a 'three-layer two-dimensional' algorithm integration model and constructs an environmental adaptability evaluation system encompassing three dimensions: technical performance, educational effectiveness, and user experience. Through a six-month empirical study conducted across 5 universities, with 180 education administrators, teachers, and technical staff as research subjects, the system performance was comprehensively evaluated using quantitative methods such as structural equation modeling and factor analysis, as well as qualitative methods including grounded theory and content analysis techniques. The findings indicate that: (1) The system's overall accuracy based on multi-integrated algorithms reached 94.8%, showing an average improvement of 6.2 percentage points compared to single algorithms, demonstrating better robustness in complex decision-making scenarios; (2) The system's environmental cognition exhibited distinct phase characteristics, with iteration frequencies decreasing from 2.5 to 1.2 times from initial adaptation to stable operation periods, maintaining adaptation accuracy above 90%; (3) Practical application results were significant, with management efficiency increasing by 45%, resource utilization improving by 28%, and user satisfaction exceeding 90% after system implementation.This research not only enriches the theoretical framework of educational decision support systems but also provides practical references for enhancing system environmental performance. However, limitations exist in sample subjects, research duration, and evaluation indicator systems. Future research needs to expand the sample scope, extend observation periods, and refine the evaluation framework.