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As the IceCube Neutrino Observatory seeks to expand its sensitivity to high PeV-EeV energies by means of the radio technique, the need for fast, efficient and reliable reconstruction methods to recover neutrino properties from radio detector data has emerged. The first recorded investigation into the possibilities of using a neural network based approach to flavor reconstruction is presented. More specifically, a deep convolutional neural network was built and optimized for the purpose of differentiating ν_e charged current (CC) interaction events from events of all other flavors and interaction modes. The approach is found to be largely successful for neutrino energies above 10¹⁸ eV, with a reported accuracy on ν_e - CC events of > 75% for neutrino energies > 10^18.5 eV while maintaining a >60% accuracy for energies > 10¹⁸. Predictive accuracy on non- ν_e - CC events varies between 80% and 90% across the considered neutrino energy range 10¹⁷<E_ν<10¹⁹. The dependence of the accuracy on ν_e - CC events on neutrino energy is pronounced and attributed to the LPM effect, which alters the features of the radio signals significantly at energies above 10¹⁸ eV in contrast to non- ν_e - CC events. The method shows promise as a first neural network based neutrino flavor reconstruction method, and results can likely be improved through further optimization.