A brief case is a brief description of a case record made by a public security organ to improve the quality of information input in the Collaborative Case Handling System and ensure efficient information retrieval and joint investigation. A large amount of case information related to the victim and the perpetrator is between various entities. Therefore, in-depth excavation of brief case texts is an effective means to grasp the beginning and end of a case and to analyze the case. The dense distribution, inter-nesting, and abbreviation of entities in a brief case text bring great challenges to the accurate capture of the case entities. In response to the particularity and complexity of brief case texts, this study improves the method of character vector generation and proposes a Roberta-CNN-BiLSTM-CRF (RC-BiLSTM-CRF) network architecture. Compared with the mainstream Bert-BiLSTM-CRF architecture, this architecture can extract the character vector features, thereby solving the problem of a lengthy character vector brought by model pre-training. The model parameter number is reduced for a higher overall parameter convergence rate. In the comparative experiment, five mainstream architectures are selected and compared on the brief case dataset provided by the public security organs of Hunan Province. The method proposed in this study is proved to be the best in terms of accuracy, recall rate, and F1 value, and its F1 value reaches 88.02%.