In this paper, traditional music time domain features and cepstrum domain features are extracted using the spectral center of mass, spectral energy, linear prediction cepstrum coefficients (LPCC) and Mel frequency cepstrum coefficients (MFCC). After that, the traditional music signal is normalized using a normalization algorithm based on the Short-Time Fourier Transform (STFT). Finally, the performance of music source separation is evaluated using NSDR. In this paper, the percentage of inheritance rate for the four parts of traditional vocal music, traditional instrumental music, and traditional drama music before digitization is less than or equal to 40%, 35.06%, and 31.25%, respectively. After digitization, their percentage of inheritance rate is greater than or equal to 86%, 93.51%, and 87.5%, respectively. The inheritance rate of the three kinds of traditional music after digitization increased in the interval of 56%. After digitization, the inheritance rate of three kinds of traditional music increased in the ranges of 56%-60%, 18.75%-31.25% and 56.25%-81.25%, respectively. This indicates that the inheritance rate of three types of traditional music increases dramatically after being processed by deep learning techniques. Obviously, the support of deep learning technology is indispensable to enhance the inheritance and innovation mechanisms of traditional music culture digitization.