Abstract: To enhance the efficiency of pressure gauge calibration and reduce reading errors, a fully intelligent pressure gauge calibration system has been developed. The system consists of an integrated AI recognition model, control software, and calibration devices, achieving full automation and intelligence in the pressure gauge calibration process. The system's recognition model is based on a deep learning network framework, integrating the YOLO detection model, Paddle OCR model, text classifier, and adjacent angle reading algorithm. This model can not only identify the pointer readings from pressure gauge images but also capture essential information such as manufacturer, serial number, accuracy class, and units. The control software is designed with a multi-threaded and asynchronous communication structure, supporting communication with multiple hardware components of the calibration device and enabling the simultaneous calibration of multiple pressure gauges. It also allows for the storage of calibration images and data for review and traceability and supports the automatic synchronization of calibration results with the OA system, along with automatic certificate printing. Experimental validation demonstrates that the system can accurately and reliably calibrate 1–6 pressure gauges simultaneously. Compared with manual calibration and other automated systems, this system offers a higher degree of intelligence, greater efficiency, and reduced reading errors, with promising applications and promotion value.