Feature Extraction and Intelligent Text Generation of Digital Music

Because the current network music operation mechanism is constantly improving and the matching of music platforms and users is poor, in this paper, the characteristics of digital music are analyzed, and the music features, rhythm, tune, intensity, and timbre with the MIDI format are extracted. Then, a music feature information extraction algorithm based on neural networks is proposed, and according to the extracted information of the music style, the B2T model is adopted for intelligent text generation. Finally, test results are given by the style matching rate and ROUGE value, which show that the model is accurate and effective for classification of music and description of related text, and the extraction of music feature information has a certain influence on its intelligent text generation.