With the development of educational technology and the accumulation of big data, student performance prediction has become a hot topic in the field of education. However, traditional manual statistical methods have limitations in dealing with complex data and are difficult to achieve high-precision prediction. To address this gap, this study proposes a clustering-based feature generation framework to enhance prediction performance. Firstly, the multilayer perceptron (MLP) model is employed to evaluate the effectiveness of the clustering algorithms (K-Means, DBSCAN, and hierarchical clustering) for feature generation. Then, the best clustering algorithm (K-Means) is applied to generate featur... More >