Abstract: With the rapid development of intelligent buildings, indoor environmental quality monitoring is crucial for ensuring the health and comfort of residents. However, the traditional production of indoor environment reports relies on manual data collection and editing, which has drawbacks such as low efficiency, long time consumption, and easy introduction of human errors, and it is difficult to meet the urgent needs of intelligent buildings for real-time monitoring and high-precision reports. To address this issue, this paper proposes an automated production method for indoor environmental reports based on OCR technology, aiming to achieve an efficient transformation from data collection to report generation. The research takes OCR technology as the core, integrates image segmentation algorithms, natural language processing, Python automated scripts, and deep learning-driven text recognition modules, and constructs a complete set of automated processing systems. By simplifying numerous cumbersome and repetitive tasks, this project has significantly improved work efficiency. Meanwhile, the accuracy and standardization level of the reports have also been enhanced accordingly. The application of this technology provides an innovative solution path for the production of indoor environment reports, and its prospects are worthy of further research and development.