The invention discloses a music automatic labeling method based on label depth analysis. The method comprises the following steps: S1, collecting music data and cleaning the data by combining a musiclabel system; S2, sampling the music data, converting the music data into a Mel-frequency spectrogram, and slicing the Mel-frequency spectrogram; S3, constructing an audio multi-level feature extraction network based on the one-dimensional convolutional network, and performing parameter pre-training through supervised learning; S4, performing music label vector representation learning based on thetwo-dimensional convolutional network, and obtaining music label characteristics; S5, realizing feature aggregation of the audio multi-level features and the music tag features; and S6, performing final music label prediction based on the aggregation characteristics. According to the method, the difficulty that a traditional music labeling mode cannot be applied to a large-scale music data set isovercome, the music is automatically labeled according to the audio content, the workload of manually maintaining a music label library is reduced, and the method has very good usability.