In Information age, the academic liberal art Computer education course set up goals for promoting computer literacy and for developing the ability to cope actively with in Information Society and for improving productivity and competition among nations. In this paper, we analyze on discovering of decisive property and satisfaction index to have a influence on computer education on university students. As a preprocessing method, the proposed method select optimum property using correlation feature selection of machine learning tool based on Java and then we use multiclass least square support vector machine based on statistical learning theory. After applying that compare with multiclass support vector machine and multiclass least square support vector machine, we can see the fact that the proposed method have a excellent result like multiclass support vector machine in analysis of the academic liberal art computer education satisfaction index data.