Due to the diverse development trend of modern media, new media arts and applications are being presented in the field of vocal performance teaching with its many advantages of interactivity, immediacy, sharing, comprehensiveness, versatility, community, and personalization. In this paper, by decoding the EEG signal, through the decoding process of EEG data pre-processing, feature extraction, feature identification, and classification, and calculating the significance of each element in the time-frequency matrix, an iso-dimensional mask matrix can be obtained. Then the conditional random field model is established on the random field theory to get the parameters of the model. Finally, the parameters of the model are obtained by maximizing the following entropy function, which is brought into the Lagrangian operator to obtain the pairwise Lagrangian operator. Finally, the EEG signal is decoded to realize the self-control training of vocal performance teaching in the new media environment. The experimental results show that by conducting the intervention test on self-control and vocal performance insight, the mean value of the total self-control score in self-control training is 61.99±11.45, and the intervention effect has stability. Therefore, improving self-control, forming correct expressions and forms, and enriching emotions are important for vocal performance.