With the development of artificial intelligence technology, it has been widely used in life and gradually penetrated judicial proceedings. However, there is insufficient interpretability in practical applications and thus it cannot effectively assist trials. In light of the four-element theory used in criminal case trials according to the constitution of a crime, this paper addresses the above problem by designing an identification task of the four elements constituting a crime. Some constituent elements of crimes of theft are screened, and a data set of the constituent elements is constructed. Moreover, a constitutive elements identification model is developed on the basis of the pre-trained language model BERT and then tested on the data set constructed in this paper, with the identification accuracy reaching 93.54%. Constructing an auxiliary sentencing algorithm based on the constituent elements can improve the interpretability of the existing algorithm and more effectively assist judges in hearing cases.