The research in this paper mainly focuses on the design of the quality assessment system of Ideological and Political Education to realize the innovation of Ideological and Political Education mode. The principal component analysis algorithm is used as the core algorithm of the assessment system, and combined with the system architecture model of hierarchical design, it realizes the collection, processing, analysis and assessment of the data on the quality of Ideological and Political classes. The research results show that the assessment system based on principal component analysis algorithm in this paper has a higher accuracy rate of education quality assessment compared to the evaluation system based on a single deep learning algorithm such as RBF neural network. At the same time, the system in this paper also has a higher assessment accuracy than the evaluation system using a combination of algorithms, and shows excellent stability performance when assessing the educational quality of 150 teachers. Using this system to assess the quality of Ideological and Political Education of 8 teachers, the comprehensive ranking is more reasonable than the original ranking. The Ideological and Political education quality assessment system designed based on the principal component analysis algorithm in this paper has a far-reaching impact on the innovation and intelligent development of the Ideological and Political Education model in the digital era.