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In order to detect the emotional state of the UAV operator in real time, a UAV operator emotional state detection algorithm analyzed based on the Two-dimensional Feature Maps and Convolutional Neural Network(CNN). Aiming at the problem of the interference comes from ocular artifacts in electroencephalogram signals(EEG), a removal algorithm of the Second Order Blinding Identification(SOBI) is designed. For the problems of low detection rates of other models, extraction of one-dimensional brain electrical signal into a two-dimensional special symbol with emotional information through the Differential Entropy (DE) extraction, 2-D Mapping mapping and sparse computing, and the electrical signal is converted into emotional information. The amplification treatment is performed, and the method of combining the Two-dimensional Feature Maps with CNN is proposed to make the emotional characteristics of each channel interconnected. Using CNN to automatically learn the advantages of deep-level characteristics, and deeply excavate the emotional information of the Electrical Electricity in the Two-dimensional Feature Maps, it has better realized the three emotional states of the UAV operator positive, neutrality and negative emotional state.