Traditional user attribute inference method is mainly based on machine learning and statistical learning methods, and its inference method ignores the user's overall representation and the correlation between tasks. A user attribute inference method based on multitasking ensemble model is proposed, which uses doc2vec unique structural characteristics and adds document vector to achieve the overall representation of the user, thus avoiding the limitations of artificial features extraction. In order to realize the multi-attribute inference task, a multi-task ensemble framework based on association learning is proposed, which is to identify multiple attributes of a user individually and give the multi-attribute representation of a single user. It enhances the overall representation of user. The relationship between multiple attributes is established at the same time, so as to improve the distinguishing degree of single-task learning. Then, this paper uses the model ensemble technology to complete the inter-attribute learning, improves the accuracy of learning and model generalization ability, and uses as few models as possible to improve the model operation efficiency. Experimental comparison on several data sets shows some advantages over other algorithms.