In the process of civic and political education, counselors should not only provide good service guidance for students’ learning but also deeply understand students’ ideological dynamics and psychological conditions. This would help to guide them in establishing healthy ideological concepts and moral qualities. The continuous development of ideological education cannot be achieved without the assistance of an experienced counselor team. There is a nationwide requirement to strengthen the professionalism of college counselors which is presently lagging. There exists a gap between the demand of the job seekers and the relevant products who fail to meet the need of the college administrators, working counselors, and other groups. The present paper focuses on providing solutions to the current problems pertinent to inaccurate matching of counselor positions in ideological and political education, the lagging information feedback, and the existence of imperfect early warning intervention mechanism. The paper proposes an integrated deep learning model which automates the learning of a large number of college students’ user behaviors using deep learning algorithms thereby incorporating early warning classifiers. This helps to establish a model enabling accurate counselor job matching and ideological and political education methods. Simulation is used to verify the effectiveness of the model using relevant databases which establishes the superiority of the proposed method in resolving mismatch issues in human resources, handles imbalances in actual effectiveness, and also ensures lagging information feedback in the process of providing dynamic early warning in case of college and university level ideological and political education.