College students have various ideological and behavioral characteristics, such as strong “self-centeredness,” which shows individuality, modernity, and holistic characteristics. Colleges and universities should explore professional education to support personalized development and cultivate new talents to take up the responsibility of national rejuvenation. In this paper, the K-Means clustering algorithm is used to initially extract and classify the behavioral characteristics of students, and the number of clustering results is classified according to the minimum error square function. The relative anomaly operator (ROCF) is used to determine the degree of anomaly for each class of campus individuals under unsupervised clustering. At the same time, the local anomaly factor algorithm LOF is combined to depict the center of the density of data points. Through the construction of an incremental algorithm, the dynamic data of student behavior is increased or decreased to establish a student’s behavioral profile. Combined with simulation experiments and empirical investigations, the ideological behavior of students is analyzed. In the comprehensive quality evaluation of students’ thoughts and behaviors, the ratings of students of the class of 2015 in the three time periods of the first semester of 2017, the second semester of 2017, and the second semester of 2018 were 68.0775, 63.8689, and 69.3028, respectively, which were decreased in the process but eventually got a small improvement. Compared to the Civics classroom, where no monitoring of abnormal student behavior was implemented, the overall quality of thought and behavior of the implemented classes of 2015 and 2016 were 68.154 and 68.455, respectively, which were higher than that of the unimplemented class of 2014.