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.