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Objective According to the characteristics of electroencephalograph (EEG), an automatic sleep level estimation method based on conditional probability is developed. The ultimate purpose is to obtain and realize the real-time sleep level evaluation. Methods There are 4 EEG channels (O2-A1，O1-A2，C4-A1，C3-A2) recorded during nap. For every 5-second data, two characteristic parameters of ratio of EEG rhythms (8-13Hz, 2-7Hz) are calculated after fast Fourier transformation (FFT). The main method consists of two models: learning and testing. During the learning stage, the probability density functions of EEG parameters are obtained based on the training data. During the testing stage, the sleep level is estimated based on the conditional probability of sleep stages. Results The nap data of 12 subjects are tested. The significant difference is analyzed. Comparing with the visual inspection of sleep stage, the obtained sleep level reflects the continuous change within or between sleep stages. The significant difference of stage awake is 2.94, stage 1 is 1.78 and stage 2 is 1.62, which fits to the regular patterns. Conclusions The defined sleep level is effective to observe the changes within the sleep stage and the transition process between the sleep stages. This method is usable for real-time nap and sleep level evaluation.