To address the issues of information overload， lack of content matching， and insufficient interpretability of the educational exam's models of English Multi-Choice Reading Comprehension （MCRC） in high school， the MCRC model integrating Knowledge augmentation and Contrastive Learning （KCL-MCRC） was proposed. First， the Large Language Model （LLM） was employed to generate a summary and extract clue sentences from the article for knowledge augmentation to reduce the input of irrelevant information. Second， the embedded representations of the article， questions and options were obtained by using the Bidirectional Encoder Representations from Transformers （BERT） model. Third， the information difference vectors of the article， questions and options were obtained through the contrastive learning strategy combining average pooling and absolute difference， and were subsequently concatenated with the original embeddings to jointly influence the answer selection. Finally， each question type was labeled by the LLM， and the performance of the KCL-MCRC model was evaluated to analyze the reasons for the model’s performance gains through accuracy in answering various types of questions. Experimental results on the RACE-H dataset show that KCL-MCRC achieves the optimal overall accuracies of 65.75% on validation set and 64.12% on test set， surpassing the models MDT （Multi-Decision-Transformer） and MMA （Multi-stage Maximization Attention） by 1.02， 0.35 and 0.88， 0.92 percentage points， respectively， achieving the best performance； both knowledge enhancement and contrastive learning are crucial for model's performance improvement， with significant the accuracy gains in three question types： logical reasoning， main idea summarization， and author's attitude. This validates effectiveness of designing modules for different question types.

It is called labels matching problem when two labels of an instance come from two labelsets respectively in multi-label classification, however there is no any specific algorithm for solving such problem. Although the labels matching problem could be solved by tranditional multi-label classification algorithms, but this problem has its own particularity. After analyzing the labels matching problem, a new labels matching algorithm based on pairwise labelsets was proposed using adaptive method, which considered the real Adaptive Boosting (real AdaBoost) and the global optimization idea. This algorithm could learn the rule of labels matching well and complete matching. The experimental results show that, compared with the traditional algorithms, the new algorithm can not only reduce searching scope of the labels space, but also decrease the minimum learning error as the number of weak classifiers increases, and make the classification more accurate and faster.