Research on the Application of Machine Learning for Marxist Education Integrating Chinese Excellent Traditional Culture

The rapid development of artificial intelligence has brought a profound impact on college education, and is triggering a profound change in educational concepts and educational methods. This project starts from the two dimensions of teaching assessment and resource recommendation of Marxist education, and centers on the application of machine learning technology in it. Based on the evaluation of Marxist education integrating Chinese traditional culture, an improved cuckoo search algorithm is used to optimize the number of hidden layer nodes of the extreme learning machine, and the ICS-ELM model is proposed. Combining similarity calculation and the k-means clustering algorithm, a recommendation model for Marxist education resources is constructed using data mining. Through experiments, it has been found that the ICS-ELM model performs better in terms of RMSE value and R value, and can better evaluate the effects of Marxist education. The response time of the Marxist education resource recommendation model is within 210ms, and the MAE and RMSE are within 0.677 and 0.823, respectively, which shows fast response speed and high accuracy of resource recommendation, which can improve the cognitive level of teachers and students and students’ learning interest. The application of machine learning technology provides a more intelligent, personalized, and efficient teaching mode for Marxist education.