Research on the Integration of Aesthetic Education Resources in Liangshan Ethnic Areas Based on Big Data Analysis

Abstract

This study explores the integration of aesthetic education resources in the Liangshan ethnic area, leveraging big data analysis techniques to address the challenges of resource fragmentation and inefficiency. Recognizing the cultural and educational significance of this region, we aim to enhance the accessibility and quality of aesthetic education. The complexity arises from the diverse and scattered nature of the resources, necessitating a robust analytical approach. We tackle this by conducting comprehensive data collection from educational institutions, cultural centers, and online platforms, followed by meticulous preprocessing, including cleaning, normalization, and transformation. Key features are extracted using TF-IDF for textual data and color histograms for visual data, which are then analyzed through advanced techniques like K-means clustering and SVM classification. Our contribution lies in formulating a weighted integration strategy that effectively combines various aesthetic education resources. To validate our approach, we evaluate the strategy using precision, recall, and F1-score metrics, achieving scores close to 0.90. The results underscore significant improvements in data quality, robust feature extraction, and the high efficacy of our big data analysis techniques, demonstrating the potential to optimize aesthetic education resources in the Liangshan ethnic area.