Question matching is an important task of question answering systems. Current methods usually use neural networks to model the semantic matching degree of two sentences. However, in the field of law, questions often have some problems, such as sparse textual representation, professional legal words, and insufficient legal knowledge contained in sentences. Therefore, the general domain deep learning text matching model is not effective in the legal question matching task. In order to make the model better understand the meaning of legal questions and model the knowledge of the legal field, this study firstly constructs a knowledge base of the legal field, and then proposes a question matching model integrating the knowledge of the legal field (such as legal words and statutes). Specifically, a legal dictionary under five categories of legal disputes has been constructed, including contract dispute, divorce, traffic accident, labor injury, debt and creditor’s right, and relevant legal articles have been collected to build a knowledge base in the legal field. In question matching, the legal knowledge base is first searched for the legal words and statutes corresponding to the question pair, and then the relationship among the question, legal words, and statutes is modeled simultaneously through the cross attention model. Finally, to achieve more accurate question matching, experiments under multiple legal categories were carried out, and the results show that the proposed method in this study can effectively improve the performance of question matching.