Zero-shot Dialogue State Tracking （DST） requires transferring the existing models to new domains without labeled data. The existing related methods often struggle to capture contextual relationships in dialogue text during domain transfer， leading to insufficient generalization of the related models when facing unknown domains. To address this issue， a zero-shot DST domain transfer model based on semantic prefix-tuning was proposed. Firstly， the slot description was utilized to generate an initial prefix， thereby ensuring close semantic connection of the prefix with the dialogue text. Secondly， the prefix position and domain information were integrated to generate a prefix that combines internal knowledge and domain information. Thirdly， the prefix length was adjusted on the basis of the complexity of dialogue content dynamically to enhance the model’s sensitivity to contextual content. Finally， global prefix insertion was employed to enhance global memory ability of the model for dialogue history. Experimental results show that compared with Prompter model， the proposed model increases the Joint Goal Accuracy （JGA） by 5.50， 0.90 and 7.50 percentage points， respectively， in the Restaurant， Taxi and Train domains of MultiWOZ2.1 dataset， and by 0.65， 14.51 and 0.65 percentage points， respectively， in the Messaging， Payment and Trains domains of SGD dataset. It can be seen that the context understanding ability and generalization transfer performance of the proposed model in DST tasks in zero-shot scenarios are improved effectively.