Abstract: Mental workload reflects people’s capability of processing information when performing a task, and it can be employed as an indicator of the brain effort. Recently, the assessment on mental workload has been widely studied in various tasks such as simulated flight, cognitive, and so on. The traditional methods of evaluating mental workload include subjective scale method, task performance method and physiological signal parameters method. Near-infrared spectroscopy (NIRS) have many advantages over other physiological techniques as it has better spatial resolution than EEG and better temporal resolution than fMRI. Besides, it is portable and lightweight with simple data acquisition and less exposed to electrical artifacts. In this paper, NIRS are selected to build an assessment model on mental workload. As well known, deep learning with convolutional neural networks has revolutionized signal processing through end-to-end learning due to its efficiency and convenience. In order to eliminate redundant information and extract features from multi-channel NIRS, a novel recognition model on mental workload is created based on the hybrid autoencoders. Firstly, the original signals are sent to the stack autoencoder for channel dimensionality reduction, and these processed signals are fed to the convolutional autoencoder to extract the abstract features. Then, we employ three base classifiers, i.e., the support vector machine (SVM), K nearest neighbors (KNN), random forest (RF), for building models. Finally, the integration strategy of soft voting and hard voting is applied to improve the assessment accuracy for mental workload. It is shown from the results that proper compressing signal channels can help improve the recognition accuracy of the recognition model. The best accuracy of this proposed model for classifying three levels of mental workload can reach 95.12%, which has significant improvement compared to similar studies.