In previous machine reading comprehension models, there were some problems, such as single-text feature extraction and incomplete interactive information between text and questions, which led to insufficient text understanding. This study proposes a machine reading understanding model with multi-level information fusion, which can obtain text information at multiple levels by using different methods in different locations. The model uses the dilated convolutional network to capture the global information of the text. Bi-directional attention mechanism and self-attention mechanism are used to fuse the interactive information between text and questions. Finally, the answer and its corresponding supporting sentence are predicted through the pointer network. The joint F1 values of the model trained on the CAIL2019 and CAIL2020 reading comprehension datasets reach 50.09% and 58.44% respectively, which achieves significant performance improvement compared with other baseline models.