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Machine reading comprehension has been an elusive challenge in the study of machine intelligence in recent years. The cloze-style reading comprehension is a query containing a placeholder that should be replaced with an appropriate word based on reading and understanding of a related document. This paper presents a novel hybrid encoding-based model for the cloze-style Chinese machine reading comprehension task, which resembles the human brain reading and reasoning process. The model is evaluated with the Children’s Fairy Tale corpus used in The First Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC2017). With an accuracy of 78.7% on the test set, this model substantially outperforms the comparison models published in CMRC2017. Also, we conduct extensive experiments on People's Daily Online, People's Daily, and Reference News datasets, and the experimental results dramatically improve a stable baseline for the Chinese machine reading comprehension task.