Ideological and political education has the teaching characteristics of keeping pace with the times. In this paper, the nonlinear support vector machine is used as the ideological and political text data classification algorithm, combined with the text mining technology to collect and screen the ideological and political education data, and the ideological and political text data is divided into various clustering centers of ideological and political education, which are reflected in the research themes of ideological and political education, the hot spots of curriculum ideological and political research, and the teaching methods of ideological and political teachers. This paper analyzes the acquisition of ideological and political education resources from the perspective of students, and explores the matching degree between the acquisition of ideological and political education resources and the individual needs of students. The research objects and research hotspots of ideological and political education are divided, and the optimization strategy of ideological and political education is proposed. In the classification of research topics, the frequency of “college students” was the highest, which was 12568, and the calorific value of the research content “ideological and political education” and the research object “college students” was 8654, indicating that ideological and political education mainly revolved around “college students”. The matching degree between ideological and political education resources and students’ individual needs was 69.37%. Combined with the results of nonlinear analysis, ideological and political education can improve the effectiveness of educational content, strengthen the coupling degree between research content and research object, and strengthen the teaching factor of teachers.