With the rapid development of deep learning technology, autism screening based on neural signals such as electroencephalography (EEG) is gradually emerging as a novel diagnostic approach. However, due to the complexity of EEG data acquisition, especially for children, insufficient data often poses a challenge. Data augmentation methods are commonly used to address the scarcity of real-world data, with generative adversarial networks (GANs) being a frequently applied technique. However, due to the limited scale and diversity of data, current augmentation methods have not yet to achieve optimal classification performance. This study introduces an improved conditional diffusion model to enhance both raw EEG signals and their corresponding functional connectivity temporal graphs. Experimental results demonstrate that this method significantly improves autism classification performance, achieving maximum classification accuracies of 84.38% and 79.01% for resting-state and task-state data, respectively. These findings validate the effectiveness of data augmentation based on the conditional diffusion model in enhancing autism screening outcomes.