The Nature of Ideological and Political Education in Colleges and Universities Based on Deep Learning Models and Its Development

Abstract

In this paper, first of all, after extracting the character sequence information, a certain scale of the corpus is obtained by using a crawler, and a corpus in the field of ideological and political education, as well as a participle system, is constructed. Then, the sequence decoding problem is solved by combining the idea of dynamic programming, based on the word level N-gram language algorithm, to design and implement an efficient solution method and calculate the final result of the participle. Finally, the keywords of related literature are classified, the essential dimensions of ideological and political education can be derived, and the essence of ideological and political education is explored and analyzed by using the word division algorithm. The results show that the most important essence of ideological and political education is “educating people’s feelings” with a weight value of 0.46, and among the 24 secondary nodes, there are 19 items with coefficients of variation less than or equal to 0.3, which shows that the degree of consistency is high, indicating that the degree of importance of the essence of ideological and political education is also high.

Keywords

• disambiguation algorithms
• character sequences
• feature sparsity
• corpus
• word-level n-grams
• 97u10