The development of Internet technology injects intelligent elements into Chinese language and literature education in Chinese universities. This paper will rely on the algorithms and models of Internet technology to create a new smart mode for Chinese language and literature education in Chinese universities. In terms of intelligent teaching, this paper adopts data mining and Web network technology to build two functional modules of online and offline processing and designs a user interest model based on multiple factors in offline analysis to further analyze the user behavior pattern so as to provide more accurate teaching support for teachers and students studying Chinese language and literature. In terms of teaching resource library design, this paper describes the process of teaching resource association based on knowledge points and, at the same time, solves the problem of manual entry in the traditional association method based on BERT text semantic encoding technology. The model can also recommend personalized teaching resources to users through the ITR model based on feature fusion. Comparative analysis indicates that the ITS model in this paper has a 6.24% increase in AUC value over the SKAT model, which is the next most effective model. The MAE values of the ITR teaching resource recommendation algorithm used in this paper on the data set with a training set ratio of 75% are 0.36, 0.40, 0.37, and 0.37, respectively, and the recommendation effect is significantly better than that of other algorithms in comparison. The inclusion of intelligent elements will promote the further development of Chinese language and literature education in Chinese universities.