Electroencephalography (EEG) provides a non-invasive, portable and low-cost way to convert neural signals into electrical signals. Using EEG to monitor people’s cognitive workload means a lot, especially for tasks demanding high attention. Before deep neural networks became a research hotspot, the use of spectrum information and the common spatial pattern algorithm (CSP) was the most popular method to classify EEG-based cognitive workloads. Recently, spectral maps have been combined with deep neural networks to achieve a final accuracy of 91.1% across four levels of cognitive workload. In this study, a parallel mechanism of spectral feature-enhanced maps is proposed which enhances the expression of structural information that may be compressed by inter- and intra-subject differences. A public dataset and milestone neural networks, such as AlexNet, VGGNet, ResNet, DenseNet are used to measure the effectiveness of this approach. As a result, the classification accuracy is improved from 91.10% to 93.71%.