The imaging quality of a telescope directly affects the reliability of astronomical research. Through the monitoring and diagnosis  of imaging quality, the cause of the deterioration of imaging quality can be found in time, which is essential for ensuring  the peaking performance of the telescope and high-quality imaging. Moreo v er, these operations are complex and crucial for  achieving high-quality imaging of future giant telescope systems involving active optics, adaptive optics, and other advanced  techniques. We propose a three-component method based on cutting-edge artificial intelligence technology to real-time monitor  and efficiently diagnose the telescope image quality. The first component, an image quality monitoring system, monitors and  outputs the telescope’s image quality. The second component is a query system with a knowledge graph, which outputs the node  chains as the possible cause of poor image quality based on the input. The third component, a final estimator, uses the node  parameter, which contains historical fault data and real-time updated data from sensors, to give the probability of each node  chain. We construct and test the system in the Large Sky Area Multi-Object Fiber Spectroscopy Telescope.