Optimization Method Based on Machine Learning for College Students’ Psychological Control Source Propensity Classification

Abstract

Abstract College students tend to have more locus of control, which is greatly affected by college students, resulting in higher classification error rate and longer classification time. An optimized method for classifying the tendency of college students’ locus of control tendency based on machine learning is proposed in this article. Collect the data of college students’ locus of control tendency, build an emotional dictionary based on it, and extract the emotional words and text features from it. According to the feature extraction results, the support vector machine is used to build a base classifier to obtain the preliminary classification results. The deep belief network is used to optimize the preliminary classification results of college students’ locus of control tendency, and the final optimization results of college students’ locus of control tendency classification are obtained. The experimental results show that the error rate of college students’ locus of control tendency classification is −1∼1 %, the average recall rate is 96.2 %, and the average classification time is 0.7 s.