Electroencephalography (EEG) contains rich information about brain function, which is very important for the detection and diagnosis of different types of neurological diseases. In view of the fact that a single feature cannot fully express the EEG signal, this paper combines frequency domain features and spatiotemporal information to better represent the signal, and proposes an attention network based on spatio-temporal and frequency domain features (STFACN) for automatic detection of Parkinson′s disease (PD). From the perspective of frequency domain, the average power characteristics of Delta, Theta and Alpha frequency bands were obtained from the multi-channel EEG using the fast Fourier transform method. In terms of spatiotemporal feature extraction, a compact convolutional neural network based on spatiotemporal features is constructed, and the channel attention mechanism is embedded into the network, and the adaptive extraction can characterize the effective features of PD. Finally, the model based on frequency domain features is fused with the compact convolutional neural network model based on spatiotemporal features, and experiments are carried out on the University of New Mexico (UNM) dataset. The specificity, sensitivity and accuracy reach 87.97%,84.39% and 86.89% respectively. Cross-dataset experiments are performed on the University of Iowa (UI) dataset, and the accuracy rate reaches 77.33%. The experimental results show that compared with the existing methods, the method proposed in this paper can mine effective features from the original EEG, and has high accuracy and strong generalization ability in the EEG-based Parkinson′s identification problem.