Speeding up the customized application of AI ideological and political model is an inevitable move to open up a new track for the development of ideological and political education and shape new advantages for the development of ideological and political education. As far as its connotation is concerned, the AI ideological and political model is a professional agent based on the general AI model, with strong relevant data or professional knowledge as training parameters, which can reflect the positions, views, and value attributes of ideological and political education. The AI ideological and political model is trained based on high-quality data or strong related professional knowledge, while the general AI model is only trained in ubiquitous data or general knowledge, which leads to a significant difference between the two. Compared with the general AI model, the performance of the AI ideological and political model is more professional, accurate, reliable, and safe. In terms of the choice of customization path, the vertical domain enhancement scheme relying on the full architecture general AI model has the advantages of a low customization threshold and less computational power consumption, which is more in line with the practical requirements of the intelligent development of ideological and political education. The scheme mainly enhances its practical performance in the ideological and political field by extracting the existing ideological and political education knowledge from the general AI model, inputting new ideological and political education knowledge into the general AI model, and integrating expert evaluation with ideological and political education. In the future, the construction of an AI ideological and political model should focus on the whole process system of “development, deployment, application, and feedback”, and enhance the “efficiency” of AI ideological and political model development by focusing on capacity building. Additionally, it should highlight the “utility” of its deployment by clarifying the application scenarios, standardize the “validity” of its application through anchoring tool orientation, and optimize the feedback “effect” by focusing on risk prevention and control.