Autism spectrum disorder is a group of complex neurological disorders that usually appear in early childhood. At present, the diagnosis of autistic children mainly relies on behavioral observation and diagnostic scales. However, some behavioral symptoms of children may not be obvious, the diagnosis results are general subjective. In order to improve the accuracy of early diagnosis and identification of autistic children, the paper proposes the diagnosis method based on temporal-spectral-spatial three-domain features and improved fast correlation based filter. Firstly, the complementarity between temporal-spectral-spatial features of EEG signals is used to analyze the brain functional network. Secondly, the improved fast correlation based filter algorithm is used to optimize the features and screen out the relevant but non-redundant features. Finally, BP-Adaboost classifier is used for identification and diagnosis. Through comparative analysis of experiments, it is found that the model has excellent effect, and the BP-Adaboost classifier has a higher identification accuracy, with an average diagnostic accuracy of 98.72%. The model can be used as an auxiliary tool to assist neurologists in diagnosing autism.