A Method for Diagnosing and Resolving Educational Opportunity Inequality Based on Big Data Analysis

In the current field of education, the issue of unequal educational opportunities is increasingly prominent, especially in terms of resource distribution and learning outcomes. This study aims to utilize big data technology to diagnose in-depth the problem of unequal opportunities in education and to explore effective solutions for sustainable development of education. We collected a wide range of educational data, including students' basic information, learning behaviors, and academic achievements, and used descriptive statistics, regression analysis, and cluster analysis to comprehensively analyze the phenomenon of educational inequality in the data. The study found that gender, geographic location, and economic background are the main factors affecting the equality of educational opportunities. Particularly in remote areas and low-income families, students significantly lag behind in accessing educational resources and academic performance. Through cluster analysis, we identified different patterns of learning behaviors and proposed targeted educational interventions based on these patterns. This research not only provides data-driven decision support for educational policymakers but also offers practical strategies for reducing educational inequalities. Future research will continue to expand the dataset and analysis methods to enhance educational fairness and promote overall societal progress.