Conformance checking is one of the important scenarios in the field of process mining, and its goal is to determine whether the actual running business behavior is consistent with the desired behavior and then provide a basis for business process management decisions. Traditional methods of conformance checking face the problems of too many metrics and low efficiency. In addition, the existing methods for checking the conformance between process text and process model rely heavily on expert-defined knowledge. Therefore, this study proposes a process text-oriented conformance checking method. Firstly, the study generates graph traces based on the execution semantics of the process model and obtains the structural features by the word vector model from graph traces. At the same time, Hoffman trees are introduced to reduce the computational effort. Then, the word vector representation of the process text and the activities is performed. The study also uses the Siamese mechanism to improve training efficiency. Finally, all the features of the text and the model are fused, and then the consistency score between the text and the model is predicted using a fully connected layer. Experiments show that the average absolute error value of the method in this study is two percentage points lower than that of existing methods.