To address the lack of sufficient paired multi-track music score datasets in the field of music representation learning， a music generation pre-training model was proposed. Firstly， a multi-generator model based on Transformers named MMGPNet （Multi-track Music Generation with Pre-training Network） as the baseline model was proposed as the fact that multi-track music generation needs to ensure continuity within the single track and harmony between the tracks at the same time. Secondly， in order to use sufficient single track musical instrument datasets， a music pre-training module was designed on the generation model. Finally， a reconstruction task was designed during the pre-training process to mask the properties of musical notations and rebuild them. Experimental results show that the proposed model accelerates training process of the model and improves the prediction accuracy. Besides， compared with baseline models such as MuseGAN （Multi-track sequential Generative Adversarial Network） and SymphonyNet， various music evaluation metrics of the generated multi-track sequences are closer to the real music. The listening test further proves the validity of the proposed model.