Construction of Mixed “Golden Course” Curriculum System of College English Based on Achievement Orientation

Accurate performance evaluation and adaptive resource allocation are essential for intelligent information processing and data-driven decision-making in modern digital learning environments. To address the limitations of conventional English teaching assessment methods, this study proposes a Golden Course-based Teaching Performance Analysis Model (GC-TPAM) that integrates Principal Component Analysis (PCA), Radial Basis Function Neural Networks (RBFNN), and Support Vector Machines (SVM) into a unified machine learning framework. PCA is employed to transform correlated evaluation indicators into representative principal components for dimensionality reduction and feature extraction, while RBFNN performs nonlinear teaching performance assessment and SVM generates personalized learning resource recommendations based on dynamic student models. The proposed architecture combines multisource educational data with adaptive evaluation and intelligent recommendation mechanisms to improve assessment accuracy and learning efficiency. Experimental results demonstrate that GC-TPAM achieves a student satisfaction ratio of 85.66% and a personalized learning ratio of 90.21%, outperforming existing comparison methods while maintaining superior teaching effectiveness and student achievement. Beyond educational applications, the proposed machine learning framework provides an effective solution for intelligent information fusion, adaptive pattern recognition, and data-driven decision optimization, offering methodological references for communication-oriented sensing systems, intelligent information processing, and engineering applications related to Electromagnetic Waves, Antennas and Propagation.