Ideological and political education is the most important part of the daily education management of college students. The methods and methods of ideological and political education are very old, and students’ learning efficiency is very low. How to use ideological and political education technology combined with modern technology for teaching has become a current research hotspot. Based on this background, this article proposes to use a new type of Internet multimedia technology interactive teaching. In the ideological and political network multimedia teaching system, use occasions suitable for IP multicast applications, such as broadcast teaching, group discussions, and on-demand courseware. Among these functions, the IP multicast mechanism is appropriately used. The adoption of the extended conversation node scales each conversation group to a multicast group, and the members of the multicast group represent the participants, which brings convenience and ease of management. Through case study analysis, it can be seen that this method can reduce the burden on the system and improve efficiency, and the number of multicast members is unlimited, which has a very good auxiliary effect on course learning. Through the Internet multimedia technology, the innovation of ideological and political education has been realized, the learning environment of students has been improved, the ways of ideological and political education have been broadened, and the education system has been better optimized.

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In this paper, an improved Extreme Gradient Boosting (XGBoost) algorithm based on the Graph Isomorphic Network (GIN) for predicting the adsorption performance of metal–organic frameworks (MOFs) is developed. It is shown that the graph isomorphic layer of this algorithm can directly learn the feature representation of materials from the connection of atoms in MOFs. Then, XGBoost can be used to predict the adsorption performance of MOFs based on feature representation. In this sense, it is not only possible to achieve end-to-end prediction directly from the structure of MOFs to adsorption performance but also to ensure the accuracy of prediction. The comparison between Grand Canonical Monte Carlo (GCMC) simulation and prediction supports the performance and effectiveness of the proposed algorithm.

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