<p>Electroencephalogram (EEG)-based analysis of emotional states is important for health assessment, emotion monitoring, and treatment support. Accurate EEG emotion recognition depends on both rich emotional features and well-designed recognition models. While deep learning can automatically extract features and classify data effectively, it often fails to produce diverse emotional EEG features, and manually designing network structures is time-consuming. Neural architecture search (NAS) helps find the best network design and reduces manual effort. In this work, we propose an attention-based convolutional neural network (CNN) architecture search method using spatial-spectral features for EEG emotion recognition, called ACAS-EER. It is optimized to better match emotional EEG characteristics. Firstly, differential entropy (DE) and power spectral density (PSD) were combined to create EEG feature maps, providing more spatial and spectral information. Secondly, a search space with four lightweight attention modules was designed. The final architecture, found automatically, includes convolution, pooling, and multiple attention modules. Also, an operation-level Dropout method was used to avoid poor performance caused by too many parameterless operations. On the DEAP dataset, ACAS-EER achieved high average accuracy and F1 scores (97.40% and 97.33% for valence; 97.68% and 97.35% for arousal). It performed better than most hand-designed deep learning models and other NAS models.</p>