During human-computer interaction, excessive mental workload is an important factor to produce operation errors. At present, EEG signals are often employed for the evaluation of mental workload based on their characteristics of high time resolution and good portability. In recent years, the rapid development of deep learning leads to its widespread application in brain electricity and better results are yielded than traditional machine learning. The n-back task can induce different degrees of psychological loads by setting different n values. In this study, the n-back paradigm based on vision and hearing is designed to avoid a single dimension. Additionally, a new convolutional neural network model is proposed. The data collected by 64-channel eego EEG equipment are preprocessed by eeglab for the training of the model. Compared with the performance of EEGNet, FBCNet, and ShallowConvNet in the test set, the classification accuracy of the proposed model is significantly improved, and thus this study has certain application potential in the evaluation of mental workload, especially in the classification of multi-dimensional n-back tasks.