Due to the rapid development of information technology, the Internet has gradually become a part of everyday life. People would like to communicate with friends to share their opinions on social networks. The diverse behavior on socials networks is an ideal reflection of users’ personality traits. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic analysis. Although they work fairly well, they are hard to extend and maintain. In this paper, we utilize a deep learning algorithm to build a feature learning model for personality prediction, which could perform an unsupervised extraction of the Linguistic Representation Feature Vector (LRFV) activity without supervision from text actively published on the Sina microblog. Compared with other feature extractsion methods, LRFV, as an abstract representation of microblog content, could describe a user’s semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using a linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of the prediction model, respectively. The results show that LRFV performs better in microblog behavior descriptions, and improves the performance of the personality prediction model.