Machine reading comprehension and question answering has long been considered as one of the core problems of natural language understanding, which requires models to select the best answer from a given text and question. With the rise of pre-trained language models such as BERT, great breakthroughs have been made in natural language processing (NLP) tasks. However, there are still some shortcomings in complex reading comprehension tasks. To solve this problem, this paper proposes a machine reading comprehension model based on retrospective readers. The proposed model uses the pre-trained model RoBERTa to encode questions and articles and divides the reading comprehension section into two modules: an intensive reading module at the word level and a comprehensive reading module at the sentence level. These two modules capture the semantic information in articles and problems at two different granularity levels. Finally, the prediction results of the two modules are combined to produce the answer with the highest probability. The model accuracy is improved in the CAIL2020 dataset and the joint-F1 value of the model reaches 66.15%, which is 5.38% higher than that of the RoBERTa model. The effectiveness of this model is proved by ablation experiments.