Artificial Intelligence Generated Content (AIGC) technology can provide various types of information generation services for humans. The current urgent issue is how to accurately evaluate the quality of AIGC. This study conducts an in-depth research on the quality of images generated by large models and their evaluation metrics. Firstly, it summarizes the common methods for evaluating AIGC from a technical perspective, such as deep learning methods and computer vision methods. It focuses on introducing the metrics used in the evaluation methods, including accuracy, relevance, consistency, and interpretability, and analyzes their performance in evaluating different types of generated content. Then, to demonstrate the practical application of these evaluation metrics, this study conducts an evaluation experiment on the images generated by ERNIE Bot as an example. Objective evaluation of the generated images is carried out using quantitative metrics such as histograms and noise counts. Subjective evaluation is performed by assessing the overall coordination and aesthetic appeal of the images. Finally, by comparing the results of objective and subjective evaluations, this study screens out highly reliable metrics for evaluating the quality of AIGC images, such as color bias, noise count, and psychological expectations. This research not only provides a theoretical reference for evaluating the quality of AIGC, but also proves the effectiveness and reliability of using both objective and subjective evaluation metrics for AIGC product evaluation through experimental results. It has certain reference significance for obtaining feedback on model performance and optimizing performance.