The development of intelligent technologies gains popularity in the education field. Educational Data Mining (EDM) is a research field of data mining, which focuses on the application of data mining, machine learning and statistical methods. The clustering effect of K-means Algorithm is tested by discriminant analysis. K-means Algorithm improves the reliability of prediction results. The development of intelligent technologies gains popularity in the education field. The rapid growth of educational data indicates traditional processing methods may have limitations and distortion. Therefore, reconstructing the research technology of data mining in the education field has become increasingly prominent. In order to avoid unreasonable evaluation results and monitor the students’ future performance in advance, this paper comprehensively uses the relevant theories of clustering, discrimination and convolution neural network to analyze and predict students’ academic performance. Firstly, this paper proposes that the clustering-number determination is optimized by using a statistic which has never been used in the algorithm of K-means.