Case reading comprehension is the specific application of machine reading comprehension in judicial field. Case reading comprehension is one of the important applications of judicial intelligence, which reads the judgment documents by computer and answers the related questions. At present, the mainstream method of machine reading comprehension is to use deep learning model to encode the text words and obtain vector representation of the text. The core problem of model construction is how to obtain the semantic representation of the text and how to match the questions with the context. Considering that syntactic information is helpful for model learning the sentence skeleton information and Chinese characters have potential semantic information, a case reading comprehension method that integrates syntactic guidance and character attention mechanism was proposed. By fusing the syntactic information and Chinese character information, the coding ability of the model for the case text was improved. Experimental results on the reading comprehension dataset of Law Research Cup 2019 show that compared with the baseline model, the proposed method has the Exact Match (EM) value increased by 0.816 and the F1 value improved by 1.809%.