With the fast development of information technology and the consequent surge in the unstructured text and audio data, traditional manual ways of processing the cases are not suitable for practical applications, which has posed great challenges to the public security organs in case investigation. Thus, this study devises the artificial intelligence-based natural language processing technology to extract and analyze the characteristic information such as reports to the police, brief cases, and records of inquiries from the information system of cases of encroachment, telecom fraud, and gang. In this way, unstructured texts can be mined and analyzed, further supporting the judgment by investigation departments and intelligence departments. Moreover, spatio-temporal information, trajectories of the crime, and the characteristics of tools and means are compared. In this way, the high-risk suspects can be found and actively recommended, greatly reducing the scope of investigation and improving the efficiency of detection.