Special equipment is extensive and widespread, and the safety situation is severe. Therefore, ensuring its safety and creditworthiness is crucial for guaranteeing public safety and people’s life and health. The national “14th Five-Year Plan” emphasizes the need to strengthen the rectification and supervision of special equipment safety. The State Administration for Market Regulation has also issued multiple documents aimed at enhancing regulatory. This paper adopts a method combining “text mining and grounded theory” to obtain dynamic monitoring indicator data for special equipment safety and credit, explore, and establish a safety and credit risk monitoring index system and calculate its weights. Based on this foundation, a special equipment safety credit status monitoring and evaluation model based on the cloud model is designed. The paper striving to provide an effective method for monitoring the safety and credit risk status of special equipment.