Research on the Influence of the Internet Technology on the Self-identity of College Students Based on Support Vector Machine Optimization Alogrithm

The college students’ self-identity refers to the process of self-exploration and self-affirmation formed. Internet technology has triggered the contradiction between real-world communication based on physical identities and virtual social communication. College students’ desire for careness and the "identity absence" in the virtual world are contradictory, causing a crisis of self-identity among college students. Meanwhile, Support Vector Machine (SVM) as one of the machine learning methods is of importance in statistical learning technology. Because of its high efficiency, good performance and wide applications in classification, forecasting, and estimation, the SVM has become the most recognized method in learning machines. Therefore, this paper applies the Support Vector Machine optimization algorithm in Matlab language to quantify the relationship between college students’ online time and identity, seeking to find a balance between virtual and real life for college students. Through the study of questionnaires, we find three characteristics of college students’ self-identity. We take these three data as the independent variables, and the average hours of weekly online time of college students as the dependent variable. By the SVM alogrithm, the model between self-identity and the hours of the network usage per week is created. Based on this, we can calculate the balance point, which means the healthy self- identity with surfing the Internet. The model shows that 40 hours should be the best hours for college students spending time online per week. Exceeding these hours will have a negative impact on the self-identity of college students. Also, it is necessary to lead college students to improve their comprehensive ability, and finally overcome the challenge of Internet technology to college students’ self-identity.