Data-Driven Intelligent Matching for the Integration of Science, Industry, and Education: Research on Path Mining and Efficacy Optimization of Achievement Transformation Based on Machine Learning Algorithms

Facing structural dilemmas in the integration of science, industry, and education---such as mismatches between achievements and demands, single transformation paths, and unbalanced regional transformation---this study constructs a data-driven intelligent matching framework. By integrating multi-source heterogeneous data, a multi-modal knowledge graph covering scientific and technological achievements, R&D personnel, enterprise institutions, and technical fields is established. On this basis, a machine learning algorithm engine integrating achievement quantitative evaluation, transformation path recommendation, and innovation ecosystem diagnosis is developed. Empirical research taking the new materials field in Shaanxi Province as a case shows that the framework achieves a path recommendation accuracy of 78.3%, increases contract value by 23.7%, shortens the transformation cycle by 26.5%, and raises the local transformation rate by 12.2 percentage points. This study provides an end-to-end solution from theoretical methods to practical applications for resolving the “matching dilemma” in the integration of science, industry, and education, and offers a new paradigm for realizing accurate, efficient, and intelligent transformation of scientific and technological achievements.