Brain functional connectivity （FC） networks serve as potential neuroimaging biomarkers for the auxiliary diagnosis and treatment of autism spectrum disorder （ASD）. However， most existing models are merely based on neuroimaging data and neglect individual clinical indicators， leading to the loss of disorder-specific information. And， ASD is a spectrum disorder exhibiting significant individual differences in terms of clinical indicators. Therefore， these traditional generative models are limited in generating accurate individual FC of ASD that reflects specific clinical symptoms. To address this limitation， a novel clinical-indicator-aware Wasserstein generative adversarial network （CI-WGAN） is proposed to generate individual FC of ASD. The proposed model introduces an effective guidance mechanism based on individual clinical indicators to generate individualized FC networks. Extensive experiments are performed on ABIDE I dataset， one of the largest publicly available ASD brain imaging datasets. The results show that the generated FC of the proposed method achieves promising peak signal-to-noise ratio （PSNR） of 19.037， structural similarity （SSIM） of 0.236 and mean absolute error （MAE） of 0.178， showing satisfying improvements of 3%， 12% and 2% respectively compared to the traditional models. Additionally， the representational similarity analysis （RSA） are performed between the generated FC and two independent clinical indicators. The results show that the RSA values based on the proposed method increase by 0.1 and 3.7 times compared to those based on traditional models， demonstrating that the FC generated via the proposed CI-WGAN contains more individual symptom information of ASD. In summary， the proposed CI-WGAN model achieves high-quality generation of individual FC， and provides a powerful tool for the early diagnosis and personalized treatment of ASD.