In the field of open social text， the generated text content lacks personalized features. In order to solve the problem， a user-level fine-grained control generation model was proposed， namely PTG-GPT2-Chinese （Personalized Text Generation Generative Pre-trained Transformer 2-Chinese）. In the proposed model， on the basis of the GPT2 （Generative Pre-trained Transformer 2.0） structure， an Encoder-Decoder model framework was designed. First， the static personalized information of a user was modeled and encoded on the Encoder side， a bidirectional independent attention module was added on the Decoder side to receive the static personalized feature vector， and the attention module in the original GPT2 structure was used for capturing the dynamic personalized features in the user’s text. Then， the scores of different attention modules were weighted and fused dynamically， and were participated in the subsequent decoding， thereby automatically generating social text constrained by the user’s personalized feature attributes. However， the semantic sparsity of the user’s basic information may cause conflicts between the generated text and some personalized features. Aiming at this problem， the BERT （Bidirectional Encoder Representations from Transformers） model was used to perform the secondary enhanced generation of consistent understanding between the output data of the Decoder side and the user’s personalized features， and finally the personalized social text generation was realized. Experimental results show that compared with the GPT2 model， the proposed model has the fluency improved by 0.36% to 0.72%， and on the basis of no loss of language fluency， the secondary generation makes the two evaluation indicators： personalization and consistency increase by 10.27% and 13.24% respectively. It is proved that the proposed model can assist user’s creation effectively and generate social text that is fluent and personalized for the user.