Research on the Influence of Socialist Core Value System Construction on College Students’ Ideological and Political Education in the Context of Deep Learning

Abstract

In this paper, the word-level N-gram language model applicable to Chinese word segmentation in ideological and political education is obtained through large-scale corpus training, and the Chinese word segmentation path in ideological and political education is calculated according to the conditional probability formula. On the basis of the deep learning algorithm, the Chinese participle algorithm is optimized by introducing word frequency deviation and sorted word frequency deviation indexes, and the cut score with the largest value of the participle indexes is taken as the final participle result output. Starting from the current situation, we determine the evaluation indexes of ideological and political education. Meanwhile, we complete the research design of the impact of the socialist core value system and use the SPSS22.0 and Origin2019 statistical software to carry out the research and analysis of ideological and political education. The results show that the index values of the Chinese word separation algorithm based on the 4-gram language model have also been improved by 1.15%, which means that there exists the possibility of practical application of the model in Chinese word separation in ideological and political education. The college students’ identification with the socialist core values at the national, social and personal levels are all at a high level, with values of 4.28±0.82, 4.32±0.75 and 4.61±0.62 respectively. This study improves students’ ideological-political literacy and provides references to their healthy growth.

Keywords

• n-gram language model
• chinese word division algorithm
• word frequency deviation
• evaluation index
• ideological and political education
• 97u10