Calibration of ionizing radiation instruments is a critical technical process to ensure the safe operation of nuclear power plants, directly impacting the reliability and responsiveness of radiation monitoring data. Traditional calibration methods involve placing the instruments in a standard irradiation field, manually reading display values, and recording data. However, these methods are inefficient, error-prone, and labor-intensive, failing to meet the modern nuclear facilities’ demands for high efficiency and precision. To address the challenges of “inaccuracy” and “inefficiency” in traditional calibration processes, this study proposes an innovative calibration method based on intelligent recognition, artificial intelligence (AI), and digital technologies, achieving full automation from data collection to processing. The system employs deep learning-based optical character recognition (OCR) technology and high-precision image processing algorithms, combined with customized outlier filtering and automated report generation modules, significantly improving calibration accuracy and efficiency. By embedding AI technology, the system addresses challenges such as display screen characteristics, lighting conditions, and alarm interference. Experimental results show that the method achieves an accuracy rate of 98.5%, with data recording efficiency improved by five times and batch calibration efficiency enhanced by up to 32 times compared to traditional methods, marking a transformative step toward intelligent calibration of ionizing radiation instruments. This study demonstrates the disruptive innovation of intelligent recognition and automated processing technologies in traditional calibration methods. Successfully applied in multiple nuclear power plants, it supports the calibration of various instrument types, significantly enhancing the safety and efficiency of nuclear facilities while providing critical technical support for the intelligent transformation of industrial metrology.