Quantitative Modeling and Machine Learning Optimization Strategies for Chinese Reading Comprehension in Language Education

Through the investigation of Chinese reading comprehension ability, the evaluation index system of Chinese reading comprehension ability is constructed, combining the hierarchical analysis method (AHP) and the data characteristic method (CRITIC) to combine the indexes to assign weights, and then using the fuzzy comprehensive evaluation model to calculate the indexes to quantify Chinese reading comprehension ability. After that, the indicators affecting Chinese reading comprehension ability in language education were screened and sorted out using a binary logistic regression model, and the Chinese reading comprehension ability education was optimized based on machine learning. This paper constructs a systematic evaluation model of Chinese reading comprehension in colleges and universities with 5 first-level indicators and 22 second-level indicators, and obtains the final score of the system of 87.73 points, the fuzzy comprehensive score of the five first-level indicators of “reading ability, general comprehension ability, deep comprehension ability, evaluation appreciation ability, and comprehensive application ability” is between 86.63 points and 88.68 points, and the fuzzy comprehensive score of 22 second-level indicators such as vocabulary, language comprehension ability and logical reasoning ability is between 80.68 points and 90.38 points. The final score of each indicator was 88.67, and the model was evaluated extremely well. In addition, the empirical analysis showed that all the indicators had a significant effect on Chinese reading comprehension (P < 0.05), and the language education should be optimized in terms of vocabulary mastery and the cultivation of critical thinking.