A fundamental problem of text-to-image synthesis is the lack of quality assessment for a single generated image. Quantitative indicators of this work (such as Inception Score and Fréchet Inception Distance) only affect plenty of images' feature distribution. It causes monotonous evaluation and plenty of poor-quality image results. This paper proposes a new evaluation criterion for text-to-image synthesis by the blind image quality assessment(BIQA) method. To train the model, a Multi-Metrics Quality Assessment Dataset for generated birds’ images(MMQA) is proposed. Besides, the Multi-hyper model is proposed to fit our dataset better. Experiments show that our method evaluates text-to-image tasks more comprehensively and optimize their results.